From a30cef36593a6840d333d209afbcf7a115102e59 Mon Sep 17 00:00:00 2001
From: jorainer <jorainer@users.noreply.github.com>
Date: Wed, 4 Dec 2024 10:53:08 +0000
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diff --git a/404.html b/404.html
index 6ea01c1..3f6c077 100644
--- a/404.html
+++ b/404.html
@@ -14,8 +14,8 @@
 <link rel="apple-touch-icon" type="image/png" sizes="60x60" href="https://rformassspectrometry.github.io/metabonaut/apple-touch-icon-60x60.png">
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 <link href="https://rformassspectrometry.github.io/metabonaut/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="https://rformassspectrometry.github.io/metabonaut/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://rformassspectrometry.github.io/metabonaut/deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="https://rformassspectrometry.github.io/metabonaut/deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="https://rformassspectrometry.github.io/metabonaut/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://rformassspectrometry.github.io/metabonaut/deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="https://rformassspectrometry.github.io/metabonaut/deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="https://rformassspectrometry.github.io/metabonaut/deps/headroom-0.11.0/headroom.min.js"></script><script src="https://rformassspectrometry.github.io/metabonaut/deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="https://rformassspectrometry.github.io/metabonaut/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="https://rformassspectrometry.github.io/metabonaut/deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="https://rformassspectrometry.github.io/metabonaut/deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="https://rformassspectrometry.github.io/metabonaut/deps/search-1.0.0/fuse.min.js"></script><script src="https://rformassspectrometry.github.io/metabonaut/deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="https://rformassspectrometry.github.io/metabonaut/pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
 <meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png">
 </head>
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="https://rformassspectrometry.github.io/metabonaut/index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/a-end-to-end-untargeted-metabolomics.html b/articles/a-end-to-end-untargeted-metabolomics.html
index 399745c..917688b 100644
--- a/articles/a-end-to-end-untargeted-metabolomics.html
+++ b/articles/a-end-to-end-untargeted-metabolomics.html
@@ -14,8 +14,8 @@
 <link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><script src="a-end-to-end-untargeted-metabolomics_files/libs/quarto-html/popper.min.js"></script><script src="a-end-to-end-untargeted-metabolomics_files/libs/quarto-html/tippy.umd.min.js"></script><link href="a-end-to-end-untargeted-metabolomics_files/libs/quarto-html/tippy.css" rel="stylesheet">
 <link href="a-end-to-end-untargeted-metabolomics_files/libs/quarto-html/light-border.css" rel="stylesheet">
 <!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Complete end-to-end LC-MS/MS Metabolomic Data analysis">
@@ -29,7 +29,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -62,10 +62,10 @@
 </nav><div class="container template-quarto">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.png" class="logo" alt=""><h1>Complete end-to-end LC-MS/MS Metabolomic Data analysis</h1>
+      <img src="../logo.png" class="logo" alt=""><h1></h1>
 
 
-      <p class="date">2024-10-30</p>
+      <p class="date">2024-12-04</p>
 
       <small class="dont-index">Source: <a href="https://github.com/rformassspectrometry/metabonaut/blob/main/vignettes/a-end-to-end-untargeted-metabolomics.qmd" class="external-link"><code>vignettes/a-end-to-end-untargeted-metabolomics.qmd</code></a></small>
       <div class="d-none name"><code></code></div>
@@ -76,7 +76,14 @@
 
 
 
-    <section class="section level2"><h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+    <header id="title-block-header" class="quarto-title-block"><h1 class="title">Complete end-to-end LC-MS/MS Metabolomic Data analysis</h1></header><div class="cell">
+<style type="text/css">
+.verysmall .table{
+  font-size: 8px;
+}
+</style>
+</div>
+<section class="section level2"><h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 <p>The present workflow describes all steps for the analysis of an LC-MS/MS experiment, which includes the preprocessing of the raw data to generate the <em>abundance</em> matrix for the <em>features</em> in the various samples, followed by data normalization, differential abundance analysis and finally the annotation of features to metabolites. Note that also alternative analysis options and R packages could be used for different steps and some examples are mentioned throughout the workflow.</p>
 <div class="quarto-figure quarto-figure-center">
@@ -481,17 +488,19 @@
 <pre><code>Filter spectra</code></pre>
 </div>
 <div class="sourceCode cell-code" id="cb14"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">bpc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span>
-<span></span>
-<span><span class="co">#' Plot after filtering</span></span>
+<code class="sourceCode R"><span><span class="va">bpc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span></code></pre></div>
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb15"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Plot after filtering</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bpc</span>, col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">80</span><span class="op">)</span>,</span>
 <span>     main <span class="op">=</span> <span class="st">"BPC after filtering retention time"</span>, lwd <span class="op">=</span> <span class="fl">1.5</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, col <span class="op">=</span> <span class="va">col_phenotype</span>,</span>
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span>, lwd <span class="op">=</span> <span class="fl">2</span>, horiz <span class="op">=</span> <span class="cn">TRUE</span>, bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/filter-rt-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/bpc2-1.png" width="672"></p>
 <figcaption>Figure 6. BPC after filtering retention time.</figcaption></figure>
 </div>
 </div>
@@ -500,7 +509,7 @@
 <p>The final plot illustrates the BPC for each sample colored by phenotype, providing insights on the signal measured along the retention times of each sample. It reveals the points at which compounds eluted from the LC column. In essence, a BPC condenses the three-dimensional LC-MS data (<em>m/z</em> by retention time by intensity) into two dimensions (retention time by intensity).</p>
 <p>We can also here compare similarities of the BPCs in a heatmap. The retention times will however not be identical between different samples. Thus we <em>bin()</em> the chromatographic signal per sample along the retention time axis into bins of two seconds resulting in data with the same number of bins/data points. We can then calculate pairwise similarities between these data vectors using the <code><a href="https://rdrr.io/r/stats/cor.html" class="external-link">cor()</a></code> function and visualize the result using <code><a href="https://rdrr.io/pkg/pheatmap/man/pheatmap.html" class="external-link">pheatmap()</a></code>.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Total ion chromatogram</span></span>
 <span><span class="va">tic</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span>, aggregationFun <span class="op">=</span> <span class="st">"sum"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">bin</a></span><span class="op">(</span>binSize <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
@@ -529,7 +538,7 @@
 <p>Throughout the entire process, it is crucial to have reference points within the dataset, such as well-known ions. Most experiments nowadays include internal standards (IS), and it was the case here. We strongly recommend using them for visualization throughout the entire analysis. For this experiment, a set of 15 IS was spiked to all samples. After reviewing signal of all of them, we selected two to guide this analysis process. However, we also advise to plot and evaluate all the ions after each steps.</p>
 <p>To illustrate this, we generate Extracted Ion Chromatograms (EIC) for these selected <em>test ions</em>. By restricting the MS data to intensities within a restricted, small <em>m/z</em> range and a selected retention time window, EICs are expected to contain only signal from a single type of ion. The expected <em>m/z</em> and retention times for our set of IS was determined in a different experiment. Additionally, in cases where internal standards are not available, commonly present ions in the sample matrix can serve as suitable alternatives. Ideally, these compounds should be distributed across the entire retention time range of the experiment.</p>
 <div class="cell" data-tbl-style="font-size: 0.8em" data-tbl-colwidths="[20,20,20]">
-<div class="sourceCode cell-code" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Load our list of standard</span></span>
 <span><span class="va">intern_standard</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html" class="external-link">read.delim</a></span><span class="op">(</span><span class="st">"intern_standard_list.txt"</span><span class="op">)</span></span>
 <span></span>
@@ -546,6 +555,8 @@
 <span><span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_is</span><span class="op">)</span><span class="op">$</span><span class="va">abbreviation</span> <span class="op">&lt;-</span> <span class="va">intern_standard</span><span class="op">$</span><span class="va">abbreviation</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_is</span><span class="op">)</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">intern_standard</span><span class="op">$</span><span class="va">abbreviation</span></span>
 <span></span>
+<span><span class="va">fdata</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_is</span><span class="op">)</span></span>
+<span></span>
 <span><span class="co">#' Summary of IS information</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_is</span><span class="op">)</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"mz"</span>, <span class="st">"rt"</span><span class="op">)</span><span class="op">]</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu">kable</span><span class="op">(</span>format <span class="op">=</span> <span class="st">"pipe"</span><span class="op">)</span></span></code></pre></div>
@@ -691,7 +702,7 @@
 </div>
 <p>We below plot the EICs for isotope labeled cystine and methionine.</p>
 <div class="cell" data-layout-align="center">
-<div class="sourceCode cell-code" id="cb17"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extract the two IS from the chromatogram object.</span></span>
 <span><span class="va">eic_cystine</span> <span class="op">&lt;-</span> <span class="va">eic_is</span><span class="op">[</span><span class="st">"cystine_13C_15N"</span><span class="op">]</span></span>
 <span><span class="va">eic_met</span> <span class="op">&lt;-</span> <span class="va">eic_is</span><span class="op">[</span><span class="st">"methionine_13C_15N"</span><span class="op">]</span></span>
@@ -710,9 +721,9 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span>v <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_met</span><span class="op">)</span><span class="op">$</span><span class="va">rt</span>, col <span class="op">=</span> <span class="st">"red"</span>, lty <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, col <span class="op">=</span> <span class="va">col_phenotype</span>, legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span>,</span>
 <span>       bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-3-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-4-1.png" width="672"></p>
 <figcaption>Figure 9. EIC of cystine and methionine.</figcaption></figure>
 </div>
 </div>
@@ -727,7 +738,7 @@
 <p>The preferred algorithm in this case, <em>CentWave</em>, utilizes continuous wavelet transformation (CWT)-based peak detection <span class="citation" data-cites="tautenhahn_highly_2008">(<a href="#ref-tautenhahn_highly_2008" role="doc-biblioref">Tautenhahn, Böttcher, and Neumann 2008</a>)</span>. This method is known for its effectiveness in handling non-Gaussian shaped chromatographic peaks or peaks with varying retention time widths, which are commonly encountered in HILIC separations.</p>
 <p>Below we apply the CentWave algorithm with its default settings on the extracted ion chromatogram for cystine and methionine ions and evaluate its results.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Use default Centwave parameter</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks-centWave.html" class="external-link">CentWaveParam</a></span><span class="op">(</span><span class="op">)</span></span>
 <span></span>
@@ -752,14 +763,14 @@
  - extendLengthMSW: [1] FALSE
  - verboseBetaColumns: [1] FALSE</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Evaluate for Cystine</span></span>
 <span><span class="va">cystine_test</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks.html" class="external-link">findChromPeaks</a></span><span class="op">(</span><span class="va">eic_cystine</span>, param <span class="op">=</span> <span class="va">param</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">cystine_test</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>     rt rtmin rtmax into intb maxo sn row column</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Evaluate for Methionine</span></span>
 <span><span class="va">met_test</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks.html" class="external-link">findChromPeaks</a></span><span class="op">(</span><span class="va">eic_met</span>, param <span class="op">=</span> <span class="va">param</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">met_test</span><span class="op">)</span></span></code></pre></div>
@@ -777,7 +788,7 @@
 <p>To determine the <code>ppm</code>, a deeper analysis of the dataset is needed. It is clarified above that <code>ppm</code> depends on the instrument, but users should not necessarily input the vendor-advertised ppm. Here’s how to determine it as accurately as possible:</p>
 <p>The following steps involve generating a highly restricted MS area with a single mass peak per spectrum, representing the cystine ion. The <em>m/z</em> of these peaks is then extracted, their absolute difference calculated and finally expressed in ppm.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb25"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Restrict the data to signal from cystine in the first sample</span></span>
 <span><span class="va">cst</span> <span class="op">&lt;-</span> <span class="va">lcms1</span><span class="op">[</span><span class="fl">1L</span><span class="op">]</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectra</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -789,7 +800,7 @@
 <div class="cell-output cell-output-stdout">
 <pre><code> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb26"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Calculate the difference in m/z values between scans</span></span>
 <span><span class="va">mz_diff</span> <span class="op">&lt;-</span> <span class="va">cst</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">mz</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -806,7 +817,7 @@
 <p>We therefore, choose a value close to the maximum within this range for parameter <code>ppm</code>, i.e., 15 ppm.</p>
 <p>We can now perform the chromatographic peak detection with the adapted settings on our EICs. It is important to note that, to properly estimate background noise, sufficient data points outside the chromatographic peak need to be present. This is generally no problem if peak detection is performed on the full LC-MS data set, but for peak detection on EICs the retention time range of the EIC needs to be sufficiently wide. If the function fails to find a peak in an EIC, the initial troubleshooting step should be to increase this range. Additionally, the signal-to-noise threshold <code>snthresh</code> should be reduced for peak detection in EICs, because within the small retention time range, not enough signal is present to properly estimate the background noise. Finally, in case of too few MS1 data points per peaks, setting CentWave’s advanced parameter <code>extendLengthMSW</code> to <code>TRUE</code> can help with peak detection.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Parameters adapted for chromatographic peak detection on EICs.</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks-centWave.html" class="external-link">CentWaveParam</a></span><span class="op">(</span>peakwidth <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span>, ppm <span class="op">=</span> <span class="fl">15</span>, integrate <span class="op">=</span> <span class="fl">2</span>,</span>
 <span>                       snthresh <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
@@ -827,7 +838,7 @@
  [9,] 208.972 206.740 212.878 28943.188 23436.119 14451.023  4   1      9
 [10,] 209.252 207.578 213.437  4470.552  3065.402  2292.881  4   1     10</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb31"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Evaluate on the methionine ion</span></span>
 <span><span class="va">met_test</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks.html" class="external-link">findChromPeaks</a></span><span class="op">(</span><span class="va">eic_met</span>, param <span class="op">=</span> <span class="va">param</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">met_test</span><span class="op">)</span></span></code></pre></div>
@@ -846,10 +857,26 @@
 </div>
 </div>
 <p>With the customized parameters, a chromatographic peak was detected in each sample. Below, we use the <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code> function on the EICs to visualize these results.</p>
-<div class="cell">
-<div class="cell-output-display">
+<div class="cell" data-layout-align="center">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb33"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Plot test chromatogram</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">cystine_test</span>, main <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">cystine_test</span><span class="op">)</span><span class="op">$</span><span class="va">name</span>,</span>
+<span>     col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">80</span><span class="op">)</span>,</span>
+<span>     peakBg <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">40</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">cystine_test</span><span class="op">)</span><span class="op">[</span>, <span class="st">"column"</span><span class="op">]</span><span class="op">]</span>,</span>
+<span>     cex.main <span class="op">=</span> <span class="fl">0.8</span>, cex.axis <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">met_test</span>, main <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">met_test</span><span class="op">)</span><span class="op">$</span><span class="va">name</span>,</span>
+<span>     col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">80</span><span class="op">)</span>,</span>
+<span>     peakBg <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">40</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">met_test</span><span class="op">)</span><span class="op">[</span>, <span class="st">"column"</span><span class="op">]</span><span class="op">]</span>,</span>
+<span>     cex.main <span class="op">=</span> <span class="fl">0.8</span>, cex.axis <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, col <span class="op">=</span> <span class="va">col_phenotype</span>,</span>
+<span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span>, bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-5-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-6-1.png" width="672"></p>
 <figcaption>Figure 11. Chromatographic peak detection on EICs.</figcaption></figure>
 </div>
 </div>
@@ -860,7 +887,7 @@
 <li><p>Parameter <code>chunkSize</code> of <code><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks.html" class="external-link">findChromPeaks()</a></code> defines the number of data files that are loaded into memory and processed simultaneously. This parameter thus allows to fine-tune the memory demand as well as performance of the chromatographic peak detection step.</p></li>
 </ul>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb32"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Using the same settings, but with default snthresh</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks-centWave.html" class="external-link">CentWaveParam</a></span><span class="op">(</span>peakwidth <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span>, ppm <span class="op">=</span> <span class="fl">15</span>, integrate <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
 <span><span class="va">lcms1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks.html" class="external-link">findChromPeaks</a></span><span class="op">(</span><span class="va">lcms1</span>, param <span class="op">=</span> <span class="va">param</span>, chunkSize <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span>
@@ -873,7 +900,7 @@
 <div class="cell-output cell-output-stderr">
 <pre><code>Processing chromatographic peaks</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_is</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eics_is_noprocess</span><span class="op">)</span></span></code></pre></div>
 </div>
 <p>Below we plot the EICs of the two selected internal standards to evaluate the chromatographic peak detection results.</p>
@@ -891,12 +918,25 @@
 <p>The identification of chromatographic peaks using the <em>CentWave</em> algorithm can sometimes result in artifacts, such as overlapping or split peaks. To address this issue, the <code><a href="https://rdrr.io/pkg/xcms/man/refineChromPeaks.html" class="external-link">refineChromPeaks()</a></code> function is utilized, in conjunction with <code>MergeNeighboringPeaksParam</code>, which aims at merging such split peaks.</p>
 <p>Below we show some examples of <em>CentWave</em> peak detection artifacts. These examples are pre-selected to illustrate the necessity of the next step:</p>
 <div class="cell">
-<div class="cell-output cell-output-stderr">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb37"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Extract m/z-rt regions for selected peaks</span></span>
+<span><span class="va">mz_rt</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/cbind.html" class="external-link">cbind</a></span><span class="op">(</span>rtmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">155.2120</span>, <span class="fl">181.71800</span><span class="op">)</span>,</span>
+<span>               rtmax <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">201.0710</span>, <span class="fl">228.13500</span><span class="op">)</span>,</span>
+<span>               mzmin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">191.0288</span>,  <span class="fl">53.50964</span><span class="op">)</span>,</span>
+<span>               mzmax <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">191.0527</span>, <span class="fl">53.53183</span><span class="op">)</span><span class="op">)</span></span>
+<span></span>
+<span><span class="co">#' Extract the EICs</span></span>
+<span><span class="va">eics</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">[</span><span class="fl">3</span><span class="op">]</span>, rt <span class="op">=</span> <span class="va">mz_rt</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtmin"</span>, <span class="st">"rtmax"</span><span class="op">)</span><span class="op">]</span>,</span>
+<span>                     mz <span class="op">=</span> <span class="va">mz_rt</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mzmin"</span>, <span class="st">"mzmax"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
+</details><div class="cell-output cell-output-stderr">
 <pre><code>Processing chromatographic peaks</code></pre>
 </div>
-<div class="cell-output-display">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb39"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Plot the EICs</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">eics</span><span class="op">)</span></span></code></pre></div>
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-6-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-7-1.png" width="672"></p>
 <figcaption>Figure 13. Examples of CentWave peak detection artifacts.</figcaption></figure>
 </div>
 </div>
@@ -917,7 +957,7 @@
 </ul>
 <p>We test these settings below on the EICs with the split peaks.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb40"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' set up the parameter</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/refineChromPeaks.html" class="external-link">MergeNeighboringPeaksParam</a></span><span class="op">(</span>expandRt <span class="op">=</span> <span class="fl">2.5</span>, expandMz <span class="op">=</span> <span class="fl">0.0015</span>,</span>
 <span>                                    minProp <span class="op">=</span> <span class="fl">0.75</span><span class="op">)</span></span>
@@ -934,13 +974,13 @@
 </div>
 <p>We can observe that the artificially split peaks have been appropriately merged. Therefore, we next apply these settings to the entire dataset. After peak merging, column <code>"merged"</code> in the result object’s <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeakData()</a></code> data frame can be used to evaluate which chromatographic peaks in the result represent signal from merged, or from the originally identified chromatographic peaks.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb37"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb41"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Apply on whole dataset</span></span>
 <span><span class="va">lcms1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/refineChromPeaks.html" class="external-link">refineChromPeaks</a></span><span class="op">(</span><span class="va">lcms1</span>, param <span class="op">=</span> <span class="va">param</span>, chunkSize <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stderr">
 <pre><code>Reduced from 106714 to 89182 chromatographic peaks.</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb39"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb43"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeakData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">merged</span> <span class="op">|&gt;</span></span>
 <span>                      <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/table.html" class="external-link">table</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -949,9 +989,9 @@
 79908  9274 </code></pre>
 </div>
 </div>
-<p>Before proceeding with the next preprocessing step it is generally suggested to evaluate the results of the chromatographic peak detection on EICs of e.g. internal standards or other compounds/ions known to be present in the samples.</p>
+<p>Before proceeding with the next preprocessing step it is generally suggested to evaluate the results of the chromatographic peak detection on EICs of e.g. internal standards or other compounds/ions known to be present in the samples. Here we update our EICs objects for the internal standards with the results of the chromatographic peak detection and refinement.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb41"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb45"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">eics_is_chrompeaks</span> <span class="op">&lt;-</span> <span class="va">eic_is</span></span>
 <span></span>
 <span><span class="va">eic_is</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span>,</span>
@@ -964,58 +1004,54 @@
 </div>
 <p>Additionally, evaluating and comparing the number of identified chromatographic peaks in all samples of a data set can help spotting potentially problematic samples. Below we count the number of chromatographic peaks per sample and show these numbers in a table.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb42"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb46"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Count the number of peaks per sample and summarize them in a table.</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>sample_name <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">sample_name</span>,</span>
 <span>           peak_count <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/table.html" class="external-link">table</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span></span>
-<span>    <span class="fu">kable</span><span class="op">(</span>format <span class="op">=</span> <span class="st">"pipe"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
-<table class="table">
+<span>  <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/t.html" class="external-link">t</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu">kable</span><span class="op">(</span>format <span class="op">=</span> <span class="st">"pipe"</span><span class="op">)</span></span></code></pre></div>
+</details><div class="cell-output-display">
+<table style="width:100%;" class="table">
 <caption>Table 5. Samples and number of identified chromatographic peaks.</caption>
-<thead><tr class="header">
-<th style="text-align: left;">sample_name</th>
-<th style="text-align: right;">peak_count</th>
-</tr></thead>
+<colgroup>
+<col style="width: 18%">
+<col style="width: 9%">
+<col style="width: 7%">
+<col style="width: 7%">
+<col style="width: 9%">
+<col style="width: 7%">
+<col style="width: 7%">
+<col style="width: 9%">
+<col style="width: 7%">
+<col style="width: 7%">
+<col style="width: 9%">
+</colgroup>
 <tbody>
 <tr class="odd">
+<td style="text-align: left;">sample_name</td>
 <td style="text-align: left;">POOL1</td>
-<td style="text-align: right;">9287</td>
-</tr>
-<tr class="even">
 <td style="text-align: left;">A</td>
-<td style="text-align: right;">8986</td>
-</tr>
-<tr class="odd">
 <td style="text-align: left;">B</td>
-<td style="text-align: right;">8738</td>
-</tr>
-<tr class="even">
 <td style="text-align: left;">POOL2</td>
-<td style="text-align: right;">9193</td>
-</tr>
-<tr class="odd">
 <td style="text-align: left;">C</td>
-<td style="text-align: right;">8351</td>
-</tr>
-<tr class="even">
 <td style="text-align: left;">D</td>
-<td style="text-align: right;">8778</td>
-</tr>
-<tr class="odd">
 <td style="text-align: left;">POOL3</td>
-<td style="text-align: right;">9211</td>
-</tr>
-<tr class="even">
 <td style="text-align: left;">E</td>
-<td style="text-align: right;">8787</td>
-</tr>
-<tr class="odd">
 <td style="text-align: left;">F</td>
-<td style="text-align: right;">8515</td>
+<td style="text-align: left;">POOL4</td>
 </tr>
 <tr class="even">
-<td style="text-align: left;">POOL4</td>
-<td style="text-align: right;">9336</td>
+<td style="text-align: left;">peak_count</td>
+<td style="text-align: left;">9287</td>
+<td style="text-align: left;">8986</td>
+<td style="text-align: left;">8738</td>
+<td style="text-align: left;">9193</td>
+<td style="text-align: left;">8351</td>
+<td style="text-align: left;">8778</td>
+<td style="text-align: left;">9211</td>
+<td style="text-align: left;">8787</td>
+<td style="text-align: left;">8515</td>
+<td style="text-align: left;">9336</td>
 </tr>
 </tbody>
 </table>
@@ -1027,8 +1063,8 @@
 </h3>
 <p>Despite using the same chromatographic settings and conditions retention time shifts are unavoidable. Indeed, the performance of the instrument can change over time, for example due to small variations in environmental conditions, such as temperature and pressure. These shifts will be generally small if samples are measured within the same batch/measurement run, but can be considerable if data of an experiment was acquired across a longer time period.</p>
 <p>To evaluate the presence of a shift we extract and plot below the BPC from the QC samples.</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb43"><pre class="downlit sourceCode r">
+<div class="cell" data-layout-align="center">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb47"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Get QC samples</span></span>
 <span><span class="va">QC_samples</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">==</span> <span class="st">"QC"</span></span>
 <span></span>
@@ -1037,9 +1073,9 @@
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span>aggregationFun <span class="op">=</span> <span class="st">"max"</span>, chromPeaks <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span>col <span class="op">=</span> <span class="va">col_phenotype</span><span class="op">[</span><span class="st">"QC"</span><span class="op">]</span>, main <span class="op">=</span> <span class="st">"BPC of QC samples"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-10-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-11-1.png" width="672"></p>
 <figcaption>Figure 15. BPC of QC samples.</figcaption></figure>
 </div>
 </div>
@@ -1061,7 +1097,7 @@
 </ul>
 <p>For the initial correspondence, parameters don’t need to be fully optimized. Selection of dataset-specific parameter values is described in more detail in the next section. For our dataset, we use small values for <code>binSize</code> and <code>ppm</code> and, importantly, also for parameter <code>bw</code>, since for our data set an ultra high performance (UHP) LC setup was used. For <code>minFraction</code> we use a high value (0.9) to ensure only features are defined for chromatographic peaks present in almost all samples of one sample group (which can then be used as anchor peaks for the actual alignment). We will base the alignment later on QC samples only and hence define for <code>sampleGroups</code> a binary variable grouping samples either into a study, or QC group.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb44"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb48"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Initial correspondence analysis</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/groupChromPeaks.html" class="external-link">PeakDensityParam</a></span><span class="op">(</span>sampleGroups <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">==</span> <span class="st">"QC"</span>,</span>
 <span>                          minFraction <span class="op">=</span> <span class="fl">0.9</span>,</span>
@@ -1077,7 +1113,7 @@
 <span>    peakPch <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-11-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-12-1.png" width="672"></p>
 <figcaption>Figure 16. Initial correspondence analysis.</figcaption></figure>
 </div>
 </div>
@@ -1090,7 +1126,7 @@
 </ul>
 <p>Below we perform the alignment of our data set based on retention times of anchor peaks defined in the subset of QC samples.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb45"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb49"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define parameters of choice</span></span>
 <span><span class="va">subset</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">==</span> <span class="st">"QC"</span><span class="op">)</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/adjustRtime.html" class="external-link">PeakGroupsParam</a></span><span class="op">(</span>minFraction <span class="op">=</span> <span class="fl">0.9</span>, extraPeaks <span class="op">=</span> <span class="fl">50</span>, span <span class="op">=</span> <span class="fl">0.5</span>,</span>
@@ -1103,7 +1139,7 @@
 <p>Alignment adjusted the retention times of all spectra in the data set, as well as the retention times of all identified chromatographic peaks.</p>
 <p>Once the alignment has been performed, the user should evaluate the results using the <code><a href="https://rdrr.io/pkg/xcms/man/plotAdjustedRtime.html" class="external-link">plotAdjustedRtime()</a></code> function. This function visualizes the difference between adjusted and raw retention time for each sample on the y-axis along the adjusted retention time on the x-axis. Dot points represent the position of the used anchor peak along the retention time axis. For optimal alignment in all areas along the retention time axis, these anchor peaks should be scattered all over the retention time dimension.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb46"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb50"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Visualize alignment results</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/plotAdjustedRtime.html" class="external-link">plotAdjustedRtime</a></span><span class="op">(</span><span class="va">lcms1</span>, col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">80</span><span class="op">)</span>, peakGroupsPch <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
@@ -1111,7 +1147,7 @@
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span>, bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-13-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-14-1.png" width="672"></p>
 <figcaption>Figure 17. Retention time alignment results.</figcaption></figure>
 </div>
 </div>
@@ -1119,15 +1155,15 @@
 <p>All samples from the present data set were measured within the same measurement run, resulting in small retention time shifts. Therefore, only little adjustments needed to be performed (shifts of at maximum 1 second as can be seen in the plot above). Generally, the magnitude of adjustment seen in such plots should match the expectation from the analyst.</p>
 <p>We can also compare the BPC before and after alignment. To get the original data, i.e. the raw retention times, we can use the <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">dropAdjustedRtime()</a></code> function:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb47"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb51"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Get data before alignment</span></span>
 <span><span class="va">lcms1_raw</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">dropAdjustedRtime</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co">#' Apply the adjusted retention time to our dataset</span></span>
 <span><span class="va">lcms1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/applyAdjustedRtime.html" class="external-link">applyAdjustedRtime</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span></span></code></pre></div>
 </div>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb48"><pre class="downlit sourceCode r">
+<div class="cell" data-layout-align="center">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb52"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Plot the BPC before and after alignment</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">0.5</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1_raw</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span>, chromPeaks <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -1142,7 +1178,7 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, col <span class="op">=</span> <span class="va">col_phenotype</span>,</span>
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span>, bty <span class="op">=</span> <span class="st">"n"</span>, horiz <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/bpc-before-and-after1-1.png" width="672"></p>
 <figcaption>Figure 18. BPC before and after alignment.</figcaption></figure>
@@ -1152,32 +1188,30 @@
 <p>The largest shift can be observed in the retention time range from 120 to 130s. Apart from that retention time range, only little changes can be observed.</p>
 <p>We next evaluate the impact of the alignment on the EICs of the selected internal standards. We thus below first extract the ion chromatograms after alignment.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb49"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co">#' Store the EICs before alignment</span></span>
-<span><span class="va">eics_is_refined</span> <span class="op">&lt;-</span> <span class="va">eic_is</span></span>
-<span></span>
-<span><span class="co">#' Update the EICs</span></span>
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb53"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Update the EICs</span></span>
 <span><span class="va">eic_is</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span>,</span>
 <span>                       rt <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="va">intern_standard</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtmin"</span>, <span class="st">"rtmax"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span>,</span>
 <span>                       mz <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="va">intern_standard</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mzmin"</span>, <span class="st">"mzmax"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_is</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eics_is_refined</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/Biobase/man/featureData.html" class="external-link">fData</a></span><span class="op">(</span><span class="va">eic_is</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">fdata</span></span>
 <span></span>
 <span><span class="co">#' Extract the EICs for the test ions</span></span>
 <span><span class="va">eic_cystine</span> <span class="op">&lt;-</span> <span class="va">eic_is</span><span class="op">[</span><span class="st">"cystine_13C_15N"</span><span class="op">]</span></span>
 <span><span class="va">eic_met</span> <span class="op">&lt;-</span> <span class="va">eic_is</span><span class="op">[</span><span class="st">"methionine_13C_15N"</span><span class="op">]</span></span></code></pre></div>
+</details>
 </div>
 <p>We can now evaluate the alignment effect in our test ions. We will plot the EICs before and after alignment for both the isotope labeled cystine and methionine.</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb50"><pre class="downlit sourceCode r">
+<div class="cell" data-layout-align="center">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb54"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">2</span><span class="op">)</span>, mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">4</span>, <span class="fl">4.5</span>, <span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">old_eic_cystine</span> <span class="op">&lt;-</span> <span class="va">eics_is_refined</span><span class="op">[</span><span class="st">"cystine_13C_15N"</span><span class="op">]</span></span>
+<span><span class="va">old_eic_cystine</span> <span class="op">&lt;-</span> <span class="va">eic_is</span><span class="op">[</span><span class="st">"cystine_13C_15N"</span><span class="op">]</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">old_eic_cystine</span>, main <span class="op">=</span> <span class="st">"Cystine before alignment"</span>, peakType <span class="op">=</span> <span class="st">"none"</span>,</span>
 <span>     col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span>v <span class="op">=</span> <span class="va">intern_standard</span><span class="op">[</span><span class="st">"cystine_13C_15N"</span>, <span class="st">"RT"</span><span class="op">]</span>, col <span class="op">=</span> <span class="st">"red"</span>, lty <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span>
 <span></span>
-<span><span class="va">old_eic_met</span> <span class="op">&lt;-</span> <span class="va">eics_is_refined</span><span class="op">[</span><span class="st">"methionine_13C_15N"</span><span class="op">]</span></span>
+<span><span class="va">old_eic_met</span> <span class="op">&lt;-</span> <span class="va">eic_is</span><span class="op">[</span><span class="st">"methionine_13C_15N"</span><span class="op">]</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">old_eic_met</span>, main <span class="op">=</span> <span class="st">"Methionine before alignment"</span>,</span>
 <span>     peakType <span class="op">=</span> <span class="st">"none"</span>, col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
@@ -1194,7 +1228,7 @@
 <span>     peakType <span class="op">=</span> <span class="st">"none"</span>, col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span>v <span class="op">=</span> <span class="va">intern_standard</span><span class="op">[</span><span class="st">"methionine_13C_15N"</span>, <span class="st">"RT"</span><span class="op">]</span>, col <span class="op">=</span> <span class="st">"red"</span>, lty <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/specific-ion-before-and-after1-1.png" width="672"></p>
 <figcaption>Figure 19. EICs of cystine and methionine before and after alignment.</figcaption></figure>
@@ -1204,7 +1238,7 @@
 <p>The non-endogenous cystine ion was already well aligned so the difference is minimal. The methionine ion, however, shows an improvement in alignment.</p>
 <p>In addition to a visual inspection of the results, we also evaluate the impact of the alignment by comparing the variance in retention times for internal standards before and after alignment. To this end, we first need to identify chromatographic peaks in each sample with an <em>m/z</em> and retention time close to the expected values for each internal standard. For this we use the <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchValues.html" class="external-link">matchValues()</a></code> function from the <em><a href="https://bioconductor.org/packages/3.20/MetaboAnnotation" class="external-link">MetaboAnnotation</a></em> package <span class="citation" data-cites="rainer_modular_2022">(<a href="#ref-rainer_modular_2022" role="doc-biblioref">Rainer et al. 2022</a>)</span> using the <code>MzRtParam</code> method to identify all chromatographic peaks with similar <em>m/z</em> (+/- 50 ppm) and retention time (+/- 10 seconds) to the internal standard’s values. With parameters <code>mzColname</code> and <code>rtColname</code> we specify the column names in the query (our IS) and target (chromatographic peaks) that contain the <em>m/z</em> and retention time values on which to match entities. We perform this matching separately for each sample. For each internal standard in every sample, we use the <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">filterMatches()</a></code> function and the <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">SingleMatchParam()</a></code> parameter to select the chromatographic peak with the highest intensity.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb51"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb55"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extract the matrix with all chromatographic peaks and add a column</span></span>
 <span><span class="co">#' with the ID of the chromatographic peak</span></span>
 <span><span class="va">chrom_peaks</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="op">)</span></span>
@@ -1232,7 +1266,7 @@
 </div>
 <p>We have now for each internal standard the ID of the chromatographic peak in each sample that most likely represents signal from that ion. We can now extract the retention times for these chromatographic peaks before and after alignment.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb52"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb56"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define the index of the selected chromatographic peaks in the</span></span>
 <span><span class="co">#' full chromPeaks matrix</span></span>
 <span><span class="va">idx</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/match.html" class="external-link">match</a></span><span class="op">(</span><span class="va">intern_standard_peaks</span>, <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
@@ -1247,7 +1281,7 @@
 </div>
 <p>We can now evaluate the impact of the alignment on the retention times of internal standards across the full data set:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb53"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb57"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>all_raw <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/matrixStats/man/rowSds.html" class="external-link">rowSds</a></span><span class="op">(</span><span class="va">rt_raw</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>,</span>
 <span>     all_adj <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/matrixStats/man/rowSds.html" class="external-link">rowSds</a></span><span class="op">(</span><span class="va">rt_adj</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span>     <span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -1255,7 +1289,7 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-18-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-19-1.png" width="672"></p>
 <figcaption>Figure 20. Retention time variation of internal standards before and after alignment.</figcaption></figure>
 </div>
 </div>
@@ -1265,7 +1299,7 @@
 </h3>
 <p>We briefly touched on the subject of correspondence before to determine anchor peaks for alignment. Generally, the goal of the correspondence analysis is to identify chromatographic peaks that originate from the same types of ions in all samples of an experiment and to group them into LC-MS <em>features</em>. At this point, proper configuration of parameter <code>bw</code> is crucial. Here we illustrate how sensible choices for this parameter’s value can be made. We use below the <code><a href="https://rdrr.io/pkg/xcms/man/plotChromPeakDensity.html" class="external-link">plotChromPeakDensity()</a></code> function to <em>simulate</em> a correspondence analysis with the default values for <em>PeakGroups</em> on the extracted ion chromatograms of our two selected isotope labeled ions. This plot shows the EIC in the top panel, and the apex position of chromatographic peaks in the different samples (y-axis), along retention time (x-axis) in the lower panel.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb54"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb58"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Default parameter for the grouping and apply them to the test ions BPC</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/groupChromPeaks.html" class="external-link">PeakDensityParam</a></span><span class="op">(</span>sampleGroups <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span>, bw <span class="op">=</span> <span class="fl">30</span><span class="op">)</span></span>
 <span></span>
@@ -1281,37 +1315,39 @@
  - maxFeatures: [1] 50
  - ppm: [1] 0</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb56"><pre class="downlit sourceCode r">
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb60"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/plotChromPeakDensity.html" class="external-link">plotChromPeakDensity</a></span><span class="op">(</span></span>
 <span>    <span class="va">eic_cystine</span>, param <span class="op">=</span> <span class="va">param</span>,</span>
 <span>    col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="st">"80"</span><span class="op">)</span>,</span>
 <span>    peakCol <span class="op">=</span> <span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_cystine</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>,</span>
 <span>    peakBg <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_cystine</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>, <span class="fl">20</span><span class="op">)</span>,</span>
 <span>    peakPch <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-19-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-21-1.png" width="672"></p>
 <figcaption>Figure 21. Initial correspondence analysis, Cystine.</figcaption></figure>
 </div>
 </div>
 </div>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb57"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb61"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/plotChromPeakDensity.html" class="external-link">plotChromPeakDensity</a></span><span class="op">(</span><span class="va">eic_met</span>, param <span class="op">=</span> <span class="va">param</span>,</span>
 <span>    col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="st">"80"</span><span class="op">)</span>,</span>
 <span>    peakCol <span class="op">=</span> <span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_met</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>,</span>
 <span>    peakBg <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_met</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>, <span class="fl">20</span><span class="op">)</span>,</span>
 <span>    peakPch <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-20-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-22-1.png" width="672"></p>
 <figcaption>Figure 22. Initial correspondence analysis, Methionine.</figcaption></figure>
 </div>
 </div>
 </div>
 <p>Grouping of peaks depends on the smoothness of the previousl mentionned density curve that can be configured with the parameter <code>bw</code>. As seen above, the smoothness is too high to properly group our features. When looking at the default parameters, we can observe that indeed, the <code>bw</code> parameter is set to <code>bw = 30</code>, which is too high for modern UHPLC-MS setups. We reduce the value of this parameter to 1.8 and evaluate its impact.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb58"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb62"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Updating parameters</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/groupChromPeaks.html" class="external-link">PeakDensityParam</a></span><span class="op">(</span>sampleGroups <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span>, bw <span class="op">=</span> <span class="fl">1.8</span><span class="op">)</span></span>
 <span></span>
@@ -1321,23 +1357,23 @@
 <span>    peakCol <span class="op">=</span> <span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_cystine</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>,</span>
 <span>    peakBg <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_cystine</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>, <span class="fl">20</span><span class="op">)</span>,</span>
 <span>    peakPch <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-21-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-23-1.png" width="672"></p>
 <figcaption>Figure 23. Correspondence analysis with optimized parameters, Cystine.</figcaption></figure>
 </div>
 </div>
 </div>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb59"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb63"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/plotChromPeakDensity.html" class="external-link">plotChromPeakDensity</a></span><span class="op">(</span><span class="va">eic_met</span>, param <span class="op">=</span> <span class="va">param</span>,</span>
 <span>    col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span>, <span class="st">"80"</span><span class="op">)</span>,</span>
 <span>    peakCol <span class="op">=</span> <span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_met</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>,</span>
 <span>    peakBg <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">eic_met</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>, <span class="fl">20</span><span class="op">)</span>,</span>
 <span>    peakPch <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-22-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-24-1.png" width="672"></p>
 <figcaption>Figure 24. Correspondence analysis with optimized parameters, Methionine.</figcaption></figure>
 </div>
 </div>
@@ -1350,7 +1386,7 @@
 <li><p><code>sampleGroups</code>: We use the information available in our <code>sampleData</code>’s <code>"phenotype"</code> column.</p></li>
 </ul>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb60"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb64"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define the settings for the param</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/groupChromPeaks.html" class="external-link">PeakDensityParam</a></span><span class="op">(</span>sampleGroups <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span>,</span>
 <span>                          minFraction <span class="op">=</span> <span class="fl">0.75</span>, binSize <span class="op">=</span> <span class="fl">0.01</span>, ppm <span class="op">=</span> <span class="fl">10</span>,</span>
@@ -1360,8 +1396,8 @@
 <span><span class="va">lcms1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/groupChromPeaks.html" class="external-link">groupChromPeaks</a></span><span class="op">(</span><span class="va">lcms1</span>, param <span class="op">=</span> <span class="va">param</span><span class="op">)</span></span></code></pre></div>
 </div>
 <p>After correspondence analysis it is suggested to evaluate the results again for selected EICs. Below we extract signal for an <em>m/z</em> similar to that of the isotope labeled methionine for a larger retention time range. Importantly, to show the actual correspondence results, we set <code>simulate = FALSE</code> for the <code><a href="https://rdrr.io/pkg/xcms/man/plotChromPeakDensity.html" class="external-link">plotChromPeakDensity()</a></code> function.</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb61"><pre class="downlit sourceCode r">
+<div class="cell" data-layout-align="center">
+<details class="code-fold"><summary>Code</summary><div class="sourceCode cell-code" id="cb65"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extract chromatogram for an m/z similar to the one of the labeled methionine</span></span>
 <span><span class="va">chr_test</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span>,</span>
 <span>                         mz <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">as.matrix</a></span><span class="op">(</span><span class="va">intern_standard</span><span class="op">[</span><span class="st">"methionine_13C_15N"</span>,</span>
@@ -1374,9 +1410,9 @@
 <span>    peakCol <span class="op">=</span> <span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">chr_test</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>,</span>
 <span>    peakBg <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">col_sample</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">chromPeaks</a></span><span class="op">(</span><span class="va">chr_test</span><span class="op">)</span><span class="op">[</span>, <span class="st">"sample"</span><span class="op">]</span><span class="op">]</span>, <span class="fl">20</span><span class="op">)</span>,</span>
 <span>    peakPch <span class="op">=</span> <span class="fl">16</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-24-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-26-1.png" width="672"></p>
 <figcaption>Figure 25. Correspondence analysis results, Methionine.</figcaption></figure>
 </div>
 </div>
@@ -1384,7 +1420,7 @@
 <p>As hoped, signal from the two different ions are now grouped into separate features. Generally, correspondence results should be evaluated on more such extracted chromatograms.</p>
 <p>Another interesting information to look a the the distribution of these features along the retention time axis.</p>
 <div class="cell" data-tbl-cap="Table 5. Distribution of features along the retention time axis.">
-<div class="sourceCode cell-code" id="cb62"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb66"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Bin features per RT slices</span></span>
 <span><span class="va">vc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">featureDefinitions</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">rtmed</span></span>
 <span><span class="va">breaks</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">vc</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span> <span class="fl">1</span>, length.out <span class="op">=</span> <span class="fl">15</span><span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -1402,7 +1438,7 @@
 </div>
 <p>The results from the correspondence analysis are now stored, along with the results from the other preprocessing steps, within our <code>XcmsExperiment</code> result object. The correspondence results, i.e., the definition of the LC-MS features, can be extracted using the <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">featureDefinitions()</a></code> function.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb64"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb68"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Definition of the features</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">featureDefinitions</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
@@ -1425,7 +1461,7 @@
 </div>
 <p>This data frame provides the average <em>m/z</em> and retention time (in columns <code>"mzmed"</code> and <code>"rtmed"</code>) that characterize a LC-MS feature. Column, <code>"peakidx"</code> contains the indices of all chromatographic peaks that were assigned to that feature. The actual abundances for these features, which represent also the final preprocessing results, can be extracted with the <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues()</a></code> function:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb66"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb70"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extract feature abundances</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues</a></span><span class="op">(</span><span class="va">lcms1</span>, method <span class="op">=</span> <span class="st">"sum"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
@@ -1458,7 +1494,7 @@
 </h3>
 <p>The previously observed missing values (<em>NA</em>) could be attributed to various reasons. Although they might represent a genuinely missing value, indicating that an ion (feature) was truly not present in a particular sample, they could also be a result of a failure in the preceding chromatographic peak detection step. It is crucial to be able to recover missing values of the latter category as much as possible to reduce the eventual need for data imputation. We next examine how prevalent missing values are in our present dataset:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb68"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb72"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Percentage of missing values</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">/</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/base/length.html" class="external-link">length</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">)</span> <span class="op">*</span> <span class="fl">100</span></span></code></pre></div>
@@ -1468,7 +1504,7 @@
 </div>
 <p>We can observe a substantial number of missing values values in our dataset. Let’s therefore delve into the process of <em>gap-filling</em>. We first evaluate some example features for which a chromatographic peak was only detected in some samples:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb70"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb74"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">ftidx</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/MatrixGenerics/man/rowSums.html" class="external-link">rowSums</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">fts</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">featureDefinitions</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">)</span><span class="op">[</span><span class="va">ftidx</span><span class="op">]</span></span>
 <span><span class="va">farea</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XcmsExperiment.html" class="external-link">featureArea</a></span><span class="op">(</span><span class="va">lcms1</span>, features <span class="op">=</span> <span class="va">fts</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">2</span><span class="op">]</span><span class="op">)</span></span>
@@ -1477,16 +1513,16 @@
 <span>             rt <span class="op">=</span> <span class="va">farea</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtmin"</span>, <span class="st">"rtmax"</span><span class="op">)</span><span class="op">]</span>,</span>
 <span>             mz <span class="op">=</span> <span class="va">farea</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mzmin"</span>, <span class="st">"mzmax"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span>col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"red"</span>, <span class="st">"blue"</span><span class="op">)</span>, lwd <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-29-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-31-1.png" width="672"></p>
 <figcaption>Figure 26. Examples of chromatographic peaks with missing values.</figcaption></figure>
 </div>
 </div>
 </div>
 <p>In both instances, a chromatographic peak was only identified in one of the two selected samples (red line), hence a missing value is reported for this feature in those particular samples (blue line). However, in both cases, signal was measured in both samples, thus, reporting a missing value would not be correct in this example. The signal for this feature is very low, which is the most likely reason peak detection failed. To rescue signal in such cases, the <code><a href="https://rdrr.io/pkg/xcms/man/fillChromPeaks.html" class="external-link">fillChromPeaks()</a></code> function can be used with the <code>ChromPeakAreaParam</code> approach. This method defines an <em>m/z</em>-retention time area for each feature based on detected peaks, where the signal for the respective ion is expected. It then integrates all intensities within this area in samples that have missing values for that feature. This is then reported as feature abundance. Below we apply this method using the default parameters.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb71"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb75"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Fill in the missing values in the whole dataset</span></span>
 <span><span class="va">lcms1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/fillChromPeaks.html" class="external-link">fillChromPeaks</a></span><span class="op">(</span><span class="va">lcms1</span>, param <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/fillChromPeaks.html" class="external-link">ChromPeakAreaParam</a></span><span class="op">(</span><span class="op">)</span>, chunkSize <span class="op">=</span> <span class="fl">5</span><span class="op">)</span></span>
 <span></span>
@@ -1501,7 +1537,7 @@
 <div class="cell">
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-31-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-33-1.png" width="672"></p>
 <figcaption>Figure 27. Examples of chromatographic peaks with missing values after gap-filling.</figcaption></figure>
 </div>
 </div>
@@ -1515,7 +1551,7 @@
 </ul>
 <p>Then, we calculate the row averages of both of these matrices and plot them against each other.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb73"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb77"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Get only detected signal in QC samples</span></span>
 <span><span class="va">vals_detect</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues</a></span><span class="op">(</span><span class="va">lcms1</span>, filled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span><span class="op">[</span>, <span class="va">QC_samples</span><span class="op">]</span></span>
 <span></span>
@@ -1539,16 +1575,16 @@
 <span>     pch <span class="op">=</span> <span class="fl">21</span>, bg <span class="op">=</span> <span class="st">"#00000020"</span>, col <span class="op">=</span> <span class="st">"#00000080"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
-<div>
+</details><div class="cell-output-display">
+<div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/Detected-vs-filled-signal1-1.png" width="672"></p>
-</figure>
+<figcaption>Figure 28. Detected vs. filled-in signal.</figcaption></figure>
 </div>
 </div>
 </div>
 <p>The detected (x-axis) and gap-filled (y-axis) values for QC samples are highly correlated. Especially for higher abundances, the agreement is very high, while for low intensities, as can be expected, differences are higher and trending to below the correlation line. Below we, in addition, fit a linear regression line to the data and summarize its results</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb74"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb78"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' fit a linear regression line to the data</span></span>
 <span><span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">avg_filled</span><span class="op">)</span> <span class="op">~</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="va">avg_detect</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">l</span><span class="op">)</span></span></code></pre></div>
@@ -1575,17 +1611,40 @@
 </div>
 </div>
 <p>The linear regression line has a slope of 1.12 and an intercept of -1.62. This indicates that the filled-in signal is on average 1.12 times higher than the detected signal.</p>
+</section><section class="level2"><h3 id="filtering-features-missing-values">Filtering Features: Missing values<a class="anchor" aria-label="anchor" href="#filtering-features-missing-values"></a>
+</h3>
+<p>We now restrict the data set to features for which a chromatographic peak was detected in at least 2/3 of samples of at least one of the study samples groups. This ensures the statistical tests carried out later on the study samples are being performed on <em>reliable</em> signal. Also, with this filter we remove features that were mostly detected in QC samples, but not the study samples. Such filter can be performed with <code><a href="https://rdrr.io/pkg/ProtGenerics/man/filterFeatures.html" class="external-link">filterFeatures()</a></code> function from the <em><a href="https://bioconductor.org/packages/3.20/xcms" class="external-link">xcms</a></em> package with the <code>PercentMissingFilter</code> setting. The parameters of this filer:</p>
+<ul>
+<li>
+<code>threshold</code>: defines the maximal acceptable percentage of samples with missing value(s) in at least one of the sample groups defined by parameter <code>f</code>.</li>
+<li>
+<code>f</code>: a factor defining the sample groups. By replacing the <code>"QC"</code> sample group with <code>NA</code> in parameter <code>f</code> we exclude the QC samples from the evaluation and consider only the study samples. With <code>threshold = 40</code> we keep only features for which a peak was detected in 2 out of the 3 samples in one of the sample groups.</li>
+</ul>
+<p>To consider detected chromatographic peaks per sample, we apply the filter on the<code>"raw"</code> assay of our result object, that contains abundance values only for detected chromatographic peaks (prior gap-filling).</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb80"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Limit features to those with at least two detected peaks in one study group.</span></span>
+<span><span class="co">#' Setting the value for QC samples to NA excludes QC samples from the</span></span>
+<span><span class="co">#' calculation.</span></span>
+<span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span></span>
+<span><span class="va">f</span><span class="op">[</span><span class="va">f</span> <span class="op">==</span> <span class="st">"QC"</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span>
+<span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span>
+<span><span class="va">lcms1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/filterFeatures.html" class="external-link">filterFeatures</a></span><span class="op">(</span><span class="va">lcms1</span>, <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/PercentMissingFilter.html" class="external-link">PercentMissingFilter</a></span><span class="op">(</span>f <span class="op">=</span> <span class="va">f</span>, threshold <span class="op">=</span> <span class="fl">40</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>344 features were removed</code></pre>
+</div>
+</div>
 </section><section class="level2"><h3 id="preprocessing-results">Preprocessing results<a class="anchor" aria-label="anchor" href="#preprocessing-results"></a>
 </h3>
 <p>The final results of the LC-MS data preprocessing are stored within the <code>XcmsExperiment</code> object. This includes the identified chromatographic peaks, the alignment results, as well as the correspondence results. In addition, to guarantee reproducibility, this result object keeps track of all performed processing steps, including the individual parameter objects used to configure these. The <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">processHistory()</a></code> function returns a list of the various applied processing steps in chronological order. Below, we extract the information for the first step of the performed preprocessing.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb76"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb82"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Check first step of the process history</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">processHistory</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>Object of class "XProcessHistory"
  type: Peak detection
- date: Wed Oct 30 16:17:12 2024
+ date: Wed Dec  4 10:43:03 2024
  info:
  fileIndex: 1,2,3,4,5,6,7,8,9,10
  Parameter class: CentWaveParam
@@ -1597,16 +1656,16 @@
 <p>As we have seen before, such feature matrix can be extracted with the <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues()</a></code> function and the corresponding feature characteristics (i.e., their <em>m/z</em> and retention time values) using the <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">featureDefinitions()</a></code> function. Thus, these two arrays could be extracted from the <em>xcms</em> result object and used/imported in other analysis packages for further processing. They could for example also be exported to tab delimited text files, and used in an external tool, or used, if also MS2 spectra were available, for feature-based molecular networking in the GNPS analysis environment <span class="citation" data-cites="nothias_feature-based_2020">(<a href="#ref-nothias_feature-based_2020" role="doc-biblioref">Nothias et al. 2020</a>)</span>.</p>
 <p>For further processing in R, if no reference or link to the raw MS data is required, it is suggested to extract the <em>xcms</em> preprocessing result using the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">quantify()</a></code> function as a <code>SummarizedExperiment</code> object, Bioconductor’s default container for data from biological assays/experiments. This simplifies integration with other Bioconductor analysis packages. The <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">quantify()</a></code> function takes the same parameters than the <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues()</a></code> function, thus, with the call below we extract a <code>SummarizedExperiment</code> with only the detected, but not gap-filled, feature abundances:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb78"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb84"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extract results as a SummarizedExperiment</span></span>
 <span><span class="va">res</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">quantify</a></span><span class="op">(</span><span class="va">lcms1</span>, method <span class="op">=</span> <span class="st">"sum"</span>, filled <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span>
 <span><span class="va">res</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>class: SummarizedExperiment
-dim: 9068 10
-metadata(6): '' '' ... '' ''
+dim: 8724 10
+metadata(7): '' '' ... '' ''
 assays(1): raw
-rownames(9068): FT0001 FT0002 ... FT9067 FT9068
+rownames(8724): FT0001 FT0002 ... FT9067 FT9068
 rowData names(11): mzmed mzmin ... QC ms_level
 colnames(10): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML ... MS_F_POS.mzML
   MS_QC_POOL_4_POS.mzML
@@ -1616,7 +1675,7 @@
 </div>
 <p>Sample identifications from the <em>xcms</em> result’s <code><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData()</a></code> are now available as <code><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">colData()</a></code> (column, sample annotations) and the <code><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">featureDefinitions()</a></code> as <code><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData()</a></code> (row, feature annotations). The feature values have been added as the first <code><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay()</a></code> in the <code>SummarizedExperiment</code> and even the processing history is available in the object’s <code><a href="https://rdrr.io/pkg/S4Vectors/man/Annotated-class.html" class="external-link">metadata()</a></code>. A <code>SummarizedExperiment</code> supports multiple assays, all being numeric matrices with the same dimensions. Below we thus add the detected and gap-filled feature abundances as an additional assay to the <code>SummarizedExperiment</code>.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb80"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb86"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assays</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">raw_filled</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-peak-grouping-results.html" class="external-link">featureValues</a></span><span class="op">(</span><span class="va">lcms1</span>, method <span class="op">=</span> <span class="st">"sum"</span>,</span>
 <span>                                        filled <span class="op">=</span> <span class="cn">TRUE</span> <span class="op">)</span></span>
 <span></span>
@@ -1628,7 +1687,7 @@
 </div>
 <p>Feature abundances can be extracted with the <code><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay()</a></code> function. Below we extract the first 6 lines of the detected and gap-filled feature abundances:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb82"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb88"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res</span>, <span class="st">"raw_filled"</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>       MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML
@@ -1656,12 +1715,12 @@
 </div>
 <p>An advantage, in addition to being a container for the full preprocessing results is also the possibility of an easy and intuitive creation of data subsets ensuring data integrity. It would for example be very easy to subset the full data to a selection of features and/or samples:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb84"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb90"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">res</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">14</span>, <span class="fl">3</span><span class="op">:</span><span class="fl">8</span><span class="op">]</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>class: SummarizedExperiment
 dim: 14 6
-metadata(6): '' '' ... '' ''
+metadata(7): '' '' ... '' ''
 assays(2): raw raw_filled
 rownames(14): FT0001 FT0002 ... FT0013 FT0014
 rowData names(11): mzmed mzmin ... QC ms_level
@@ -1673,7 +1732,7 @@
 </div>
 <p>Before moving to the next step of the analysis, it is advisable to save the preprocessing results. We have multiple format options to save into, and they can be found in the <code>MsIO</code> package <a href="https://rformassspectrometry.github.io/MsIO/articles/MsIO.html" class="external-link">documentation</a>. Below we will save our <code>XcmsExperiment</code> object into a file format handled by the <a href="https://github.com/ArtifactDB/alabaster.base" class="external-link">alabster framework</a>, which ensures that our object can be easily read from other languages like Python and Javascript as well as loaded easily back into R.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb86"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb92"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' XcmsExperiment object: </span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/MsIO/man/saveMsObject.html" class="external-link">saveMsObject</a></span><span class="op">(</span><span class="va">lcms1</span>,</span>
 <span>             <span class="fu"><a href="https://rdrr.io/pkg/MsIO/man/AlabasterParam.html" class="external-link">AlabasterParam</a></span><span class="op">(</span>path <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/file.path.html" class="external-link">file.path</a></span><span class="op">(</span><span class="st">"objects/preprocessed_lcms1"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
@@ -1700,12 +1759,8 @@
 <p>A principal component analysis (PCA) is a very helpful tool for an initial, unsupervised, visualization of the data that also provides insights into potential quality issues in the data. In order to apply a PCA to the measured feature abundances, we need however to impute (the still present) missing values. We assume that most of these missing values (after the gap-filling step) represent signal which is below detection limit. In such cases, missing values can be replaced with random values sampled from a uniform distribution, ranging from half of the smallest measured value to the smallest measured value for a specific feature. The uniform distribution is defined with two parameters (minimum and maximum) and all values between them have an equal probability of being selected.</p>
 <p>Below we impute missing values with this approach and add the resulting data matrix as a new <em>assay</em> to our result object.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb87"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co">#' Load preprocessing results</span></span>
-<span><span class="co">## load("SumExp.RData")</span></span>
-<span><span class="co">## loadResults(RDataParam("data.RData"))</span></span>
-<span></span>
-<span><span class="co">#' Impute missing values using an uniform distribution</span></span>
+<div class="sourceCode cell-code" id="cb93"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' Impute missing values using an uniform distribution</span></span>
 <span><span class="va">na_unidis</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">z</span><span class="op">)</span> <span class="op">{</span></span>
 <span>    <span class="va">na</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">z</span><span class="op">)</span></span>
 <span>    <span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html" class="external-link">any</a></span><span class="op">(</span><span class="va">na</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span>
@@ -1724,7 +1779,7 @@
 <p>PCA is a powerful tool for detecting biases in data. As a dimensionality reduction technique, it enables visualization of data in a lower-dimensional space. In the context of LC-MS data, PCA can be used to identify overall biases in batch, sample, injection index, etc. However, it is important to note that PCA is a linear method and may not be able to detect all biases in the data.</p>
 <p>Before plotting the PCA, we apply a log2 transform, center and scale the data. The log2 transformation is applied to stabilize the variance while centering to remove dependency on absolute abundances.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb88"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb94"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Log2 transform and scale data</span></span>
 <span><span class="va">vals</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res</span>, <span class="st">"raw_filled_imputed"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log2</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -1742,7 +1797,7 @@
 <span>                   x <span class="op">=</span> <span class="fl">3</span>, y <span class="op">=</span> <span class="fl">4</span>, scale <span class="op">=</span> <span class="fl">0</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_color_manual</a></span><span class="op">(</span>values <span class="op">=</span> <span class="va">col_phenotype</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/gridExtra/man/arrangeGrob.html" class="external-link">grid.arrange</a></span><span class="op">(</span><span class="va">pca_12</span>, <span class="va">pca_34</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unsupervised-checks1-1.png" width="576"></p>
 <figcaption>Figure 28. PCA of the data coloured by phenotypes.</figcaption></figure>
@@ -1755,7 +1810,7 @@
 </h4>
 <p>Global differences in feature abundances between samples (e.g. due to sample-specific biases) can be evaluated by plotting the distribution of the log2 transformed feature abundances using boxplots of violin plots. Below we show the number of detected chromatographic peaks per sample and the distribution of the log2 transformed feature abundances.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb89"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb95"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/layout.html" class="external-link">layout</a></span><span class="op">(</span>mat <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html" class="external-link">matrix</a></span><span class="op">(</span><span class="fl">1</span><span class="op">:</span><span class="fl">3</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span>, height <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">0.2</span>, <span class="fl">0.8</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">0.2</span>, <span class="fl">4.5</span>, <span class="fl">0.2</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
@@ -1777,7 +1832,7 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, col <span class="op">=</span> <span class="va">col_phenotype</span>,</span>
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty<span class="op">=</span><span class="fl">1</span>, lwd <span class="op">=</span> <span class="fl">2</span>, xpd <span class="op">=</span> <span class="cn">TRUE</span>, ncol <span class="op">=</span> <span class="fl">3</span>,</span>
 <span>       cex <span class="op">=</span> <span class="fl">0.8</span>,  bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/counts1-1.png" width="672"></p>
 <figcaption>Figure 29. Number of detected peaks and feature abundances.</figcaption></figure>
@@ -1787,7 +1842,7 @@
 <p>The upper part of the plot show that the gap filling steps allowed to rescue a substantial number of <em>NAs</em> and allowed us to have a more consistent number of feature values per sample. This consistency aligns with our asspumption that every sample should have a similar amount of features detected. Additionally we observe that, on average, the signal distribution from the individual samples is very similar.</p>
 <p>An alternative way to evaluate differences in abundances between samples are <em>relative log abundance</em> (RLA) plots <span class="citation" data-cites="de_livera_normalizing_2012">(<a href="#ref-de_livera_normalizing_2012" role="doc-biblioref">De Livera et al. 2012</a>)</span>. An RLA value is the abundance of a feature in a sample relative to the median abundance of the same feature across multiple samples. We can discriminate between <em>within group</em> and <em>across group</em> RLAs, depending whether the abundance is compared against samples within the same sample group or across all samples. Within group RLA plots assess the tightness of replicates within groups and should have a median close to zero and low variation around it. When used across groups, they allow to compare behavior between groups. Generally, between-sample differences can be easily spotted using RLA plots. Below we calculate and visualize within group RLA values using the <code><a href="https://rdrr.io/pkg/xcms/man/rla.html" class="external-link">rowRla()</a></code> function from the <em><a href="https://bioconductor.org/packages/3.20/MsCoreUtils" class="external-link">MsCoreUtils</a></em> package defining with parameter <code>f</code> the sample groups.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb90"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb96"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3.5</span>, <span class="fl">4.5</span>, <span class="fl">2.5</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></span><span class="op">(</span><span class="fu">MsCoreUtils</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/MsCoreUtils/man/rla.html" class="external-link">rowRla</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res</span>, <span class="st">"raw_filled"</span><span class="op">)</span>,</span>
 <span>                            f <span class="op">=</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span>, transform <span class="op">=</span> <span class="st">"log2"</span><span class="op">)</span>,</span>
@@ -1802,7 +1857,7 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, col <span class="op">=</span> <span class="va">col_phenotype</span>,</span>
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty<span class="op">=</span><span class="fl">1</span>, lwd <span class="op">=</span> <span class="fl">2</span>, xpd <span class="op">=</span> <span class="cn">TRUE</span>, ncol <span class="op">=</span> <span class="fl">3</span>,</span>
 <span>       cex <span class="op">=</span> <span class="fl">0.8</span>,  bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/rla-plot-raw-and-filled1-1.png" width="672"></p>
 <figcaption>Figure 30. RLA plot for the raw data and filled data.</figcaption></figure>
@@ -1818,7 +1873,7 @@
 <p>This method involves computing the median for each sample, followed by determining the median of these individual sample medians. This ensures consistent median values for each sample throughout the entire data set. Maintaining uniformity in the average total metabolite abundance across all samples is crucial for effective implementation.</p>
 <p>This process aims to establish a shared baseline for the central tendency of metabolite abundance, mitigating the impact of sample-specific technical variations. This approach fosters a more robust and comparable analysis of the top features across the data set. The assumption is that normalizing based on the median, known for its lower sensitivity to extreme values, enhances the comparability of top features and ensures a consistent average abundance across samples.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb91"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb97"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Compute median and generate normalization factor</span></span>
 <span><span class="va">mdns</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/apply.html" class="external-link">apply</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res</span>, <span class="st">"raw_filled"</span><span class="op">)</span>, MARGIN <span class="op">=</span> <span class="fl">2</span>,</span>
 <span>              <span class="va">median</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span> <span class="op">)</span></span>
@@ -1836,7 +1891,7 @@
 <section class="level3"><h4 id="principal-component-analysis-1">Principal Component Analysis<a class="anchor" aria-label="anchor" href="#principal-component-analysis-1"></a>
 </h4>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb92"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb98"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Data before normalization</span></span>
 <span><span class="va">vals_st</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="va">vals</span>, phenotype <span class="op">=</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span><span class="op">)</span></span>
 <span><span class="va">pca_raw</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">pca_res</span>, data <span class="op">=</span> <span class="va">vals_st</span>,</span>
@@ -1855,16 +1910,16 @@
 <span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_color_manual</a></span><span class="op">(</span>values <span class="op">=</span> <span class="va">col_phenotype</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/gridExtra/man/arrangeGrob.html" class="external-link">grid.arrange</a></span><span class="op">(</span><span class="va">pca_raw</span>, <span class="va">pca_adj</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-40-1.png" width="576"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-43-1.png" width="576"></p>
 <figcaption>Figure 31. PC1 and PC2 of the data before and after normalization.</figcaption></figure>
 </div>
 </div>
 </div>
 <p>Normalization did not have a large impact on PC1 or PC2, but the separation of the study groups on PC3 seems to be better and difference between QC samples lower after normalization (see below).</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb93"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb99"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">pca_raw</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">pca_res</span>, data <span class="op">=</span> <span class="va">vals_st</span> ,</span>
 <span>                    colour <span class="op">=</span> <span class="st">'phenotype'</span>, x <span class="op">=</span> <span class="fl">3</span>, y <span class="op">=</span> <span class="fl">4</span>, scale <span class="op">=</span> <span class="fl">0</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_color_manual</a></span><span class="op">(</span>values <span class="op">=</span> <span class="va">col_phenotype</span><span class="op">)</span></span>
@@ -1873,9 +1928,9 @@
 <span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_color_manual</a></span><span class="op">(</span>values <span class="op">=</span> <span class="va">col_phenotype</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/gridExtra/man/arrangeGrob.html" class="external-link">grid.arrange</a></span><span class="op">(</span><span class="va">pca_raw</span>, <span class="va">pca_adj</span>, ncol <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-41-1.png" width="576"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-44-1.png" width="576"></p>
 <figcaption>Figure 32. PC3 and PC4 of the data before and after normalization.</figcaption></figure>
 </div>
 </div>
@@ -1885,7 +1940,7 @@
 </h4>
 <p>We compare the RLA plots before and after between-sample normalization to evaluate its impact on the data.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb94"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb100"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>, mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">3.5</span>, <span class="fl">4.5</span>, <span class="fl">2.5</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/boxplot.html" class="external-link">boxplot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/rla.html" class="external-link">rowRla</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res</span>, <span class="st">"raw_filled"</span><span class="op">)</span>, group <span class="op">=</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span><span class="op">)</span>,</span>
@@ -1905,9 +1960,9 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/axis.html" class="external-link">axis</a></span><span class="op">(</span>side <span class="op">=</span> <span class="fl">1</span>, at <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_len</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/nrow.html" class="external-link">ncol</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">)</span>, labels <span class="op">=</span> <span class="va">res</span><span class="op">$</span><span class="va">sample_name</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span>nx <span class="op">=</span> <span class="cn">NA</span>, ny <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span>h <span class="op">=</span> <span class="fl">0</span>, lty <span class="op">=</span> <span class="fl">3</span>, lwd <span class="op">=</span> <span class="fl">1</span>, col <span class="op">=</span> <span class="st">"black"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/rla-plot-after-norm2-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/rla-plot-after-norm2-1.png" width="576"></p>
 <figcaption>Figure 33. RLA plot before and after normalization.</figcaption></figure>
 </div>
 </div>
@@ -1917,7 +1972,7 @@
 </h4>
 <p>We next evaluate the coefficient of variation (CV, also referred to as <em>relative standard deviation</em> RSD) for features across samples from either QC or study samples. In QC samples, the CV would represent technical noise, while in study samples it would include also expected biological differences. Thus, normalization should reduce the CV in QC samples, while only slightly reducing the CV in study samples. The CV is calculated below using the <code><a href="https://rdrr.io/pkg/MetaboCoreUtils/man/quality_assessment.html" class="external-link">rowRsd()</a></code> function from the <em><a href="https://bioconductor.org/packages/3.20/MetaboCoreUtils" class="external-link">MetaboCoreUtils</a></em> package. By setting <code>mad = TRUE</code> we use a more robust calculation using the median absolute deviation instead of the standard deviation.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb95"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb101"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Calculate the CV values</span></span>
 <span><span class="va">index_study</span> <span class="op">&lt;-</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span> <span class="op"><a href="https://rdrr.io/pkg/BiocGenerics/man/match.html" class="external-link">%in%</a></span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CTR"</span>, <span class="st">"CVD"</span><span class="op">)</span></span>
 <span><span class="va">index_QC</span> <span class="op">&lt;-</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">==</span> <span class="st">"QC"</span></span>
@@ -1942,7 +1997,7 @@
 <span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu">kable</span><span class="op">(</span><span class="va">res_df</span>, format <span class="op">=</span> <span class="st">"pipe"</span><span class="op">)</span></span></code></pre></div>
-<table class="table">
+</details><table class="table">
 <caption>Table 6. Distribution of CV values across samples for the raw and normalized data.</caption>
 <thead><tr class="header">
 <th style="text-align: left;"></th>
@@ -1956,34 +2011,34 @@
 <td style="text-align: left;">0%</td>
 <td style="text-align: right;">0.0000000</td>
 <td style="text-align: right;">0.0000000</td>
-<td style="text-align: right;">0.0000000</td>
-<td style="text-align: right;">0.0000000</td>
+<td style="text-align: right;">0.0008024</td>
+<td style="text-align: right;">0.0027477</td>
 </tr>
 <tr class="even">
 <td style="text-align: left;">25%</td>
-<td style="text-align: right;">0.0455681</td>
-<td style="text-align: right;">0.0451006</td>
-<td style="text-align: right;">0.1362988</td>
-<td style="text-align: right;">0.1373298</td>
+<td style="text-align: right;">0.0450743</td>
+<td style="text-align: right;">0.0446309</td>
+<td style="text-align: right;">0.1423902</td>
+<td style="text-align: right;">0.1430977</td>
 </tr>
 <tr class="odd">
 <td style="text-align: left;">50%</td>
-<td style="text-align: right;">0.0975842</td>
-<td style="text-align: right;">0.0971473</td>
-<td style="text-align: right;">0.2634885</td>
-<td style="text-align: right;">0.2622390</td>
+<td style="text-align: right;">0.0972048</td>
+<td style="text-align: right;">0.0970785</td>
+<td style="text-align: right;">0.2668930</td>
+<td style="text-align: right;">0.2658062</td>
 </tr>
 <tr class="even">
 <td style="text-align: left;">75%</td>
-<td style="text-align: right;">0.2002930</td>
-<td style="text-align: right;">0.1987053</td>
-<td style="text-align: right;">0.4859210</td>
-<td style="text-align: right;">0.4824231</td>
+<td style="text-align: right;">0.2003657</td>
+<td style="text-align: right;">0.1985980</td>
+<td style="text-align: right;">0.4867303</td>
+<td style="text-align: right;">0.4825679</td>
 </tr>
 <tr class="odd">
 <td style="text-align: left;">100%</td>
 <td style="text-align: right;">1.4635996</td>
-<td style="text-align: right;">1.4637306</td>
+<td style="text-align: right;">1.4637193</td>
 <td style="text-align: right;">1.4825882</td>
 <td style="text-align: right;">1.4825893</td>
 </tr>
@@ -1999,43 +2054,21 @@
 <p>After normalizing our data we can now pre-filter to clean the data before performing any statistical analysis. In general, pre-filtering of samples and features is performed to remove outliers.</p>
 <p>Below we copy the original result object to also keep the unfiltered data for later comparisons.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb96"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb102"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Number of features before filtering</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 9068</code></pre>
+<pre><code>[1] 8724</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb98"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb104"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' keep unfiltered object</span></span>
 <span><span class="va">res_unfilt</span> <span class="op">&lt;-</span> <span class="va">res</span></span></code></pre></div>
 </div>
 <p>Here we will eliminate features that exhibit high variability in our dataset. Repeatedly measured QC samples typically serve as a robust basis for cleansing datasets allowing to identify features with excessively high noise. As in our data set external QC samples were used, i.e. pooled samples from a different collection and using a slightly different sample matrix, their utility for filtering is somewhat limited. For a comprehensive description and guidelines for data filtering in untargeted metabolomic studies, please refer to <span class="citation" data-cites="broadhurst_guidelines_2018">(<a href="#ref-broadhurst_guidelines_2018" role="doc-biblioref">Broadhurst et al. 2018</a>)</span>.</p>
-<p>We first restrict the data set to features for which a chromatographic peak was detected in at least 2/3 of samples of at least one of the study samples groups. This ensures the statistical tests carried out later on the study samples are being performed on <em>reliable</em> signal. Also, with this filter we remove features that were mostly detected in QC samples, but not the study samples. Such filter can be performed with <code><a href="https://rdrr.io/pkg/ProtGenerics/man/filterFeatures.html" class="external-link">filterFeatures()</a></code> function from the <em><a href="https://bioconductor.org/packages/3.20/xcms" class="external-link">xcms</a></em> package with the <code>PercentMissingFilter</code> setting. The parameters of this filer:</p>
-<ul>
-<li>
-<code>threshold</code>: defines the maximal acceptable percentage of samples with missing value(s) in at least one of the sample groups defined by parameter <code>f</code>.</li>
-<li>
-<code>f</code>: a factor defining the sample groups. By replacing the <code>"QC"</code> sample group with <code>NA</code> in parameter <code>f</code> we exclude the QC samples from the evaluation and consider only the study samples. With <code>threshold = 40</code> we keep only features for which a peak was detected in 2 out of the 3 samples in one of the sample groups.</li>
-</ul>
-<p>To consider detected chromatographic peaks per sample, we apply the filter on the<code>"raw"</code> assay of our result object, that contains abundance values only for detected chromatographic peaks (prior gap-filling).</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb99"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co">#' Limit features to those with at least two detected peaks in one study group.</span></span>
-<span><span class="co">#' Setting the value for QC samples to NA excludes QC samples from the</span></span>
-<span><span class="co">#' calculation.</span></span>
-<span><span class="va">f</span> <span class="op">&lt;-</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span></span>
-<span><span class="va">f</span><span class="op">[</span><span class="va">f</span> <span class="op">==</span> <span class="st">"QC"</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span>
-<span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">f</span><span class="op">)</span></span>
-<span><span class="va">res</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/filterFeatures.html" class="external-link">filterFeatures</a></span><span class="op">(</span><span class="va">res</span>, <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/PercentMissingFilter.html" class="external-link">PercentMissingFilter</a></span><span class="op">(</span>f <span class="op">=</span> <span class="va">f</span>, threshold <span class="op">=</span> <span class="fl">40</span><span class="op">)</span>,</span>
-<span>                      assay <span class="op">=</span> <span class="st">"raw"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>1808 features were removed</code></pre>
-</div>
-</div>
 <p>Following the guidelines stated above we decided to still use the QC samples for pre-filtering, on the basis that they represent similar bio-fluids to our study samples, and thus, we anticipate observing relatively similar metabolites affected by similar measurement biases.</p>
 <p>We therefore evaluate the <em>dispersion ratio</em> (Dratio) <span class="citation" data-cites="broadhurst_guidelines_2018">(<a href="#ref-broadhurst_guidelines_2018" role="doc-biblioref">Broadhurst et al. 2018</a>)</span> for all features in the data set. We accomplish this task using the same function as above but this time with the <code>DratioFilter</code> parameter. More filters exist for this function and we invite the user to explore them as to decide what is best for their dataset.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb101"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb105"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Compute and filter based on the Dratio</span></span>
 <span><span class="va">filter_dratio</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/DratioFilter.html" class="external-link">DratioFilter</a></span><span class="op">(</span>threshold <span class="op">=</span> <span class="fl">0.4</span>,</span>
 <span>                              qcIndex <span class="op">=</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">==</span> <span class="st">"QC"</span>,</span>
@@ -2043,46 +2076,63 @@
 <span>                              mad <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
 <span><span class="va">res</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/filterFeatures.html" class="external-link">filterFeatures</a></span><span class="op">(</span><span class="va">res</span>, filter <span class="op">=</span> <span class="va">filter_dratio</span>, assay <span class="op">=</span> <span class="st">"norm_imputed"</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stderr">
-<pre><code>2983 features were removed</code></pre>
+<pre><code>4205 features were removed</code></pre>
 </div>
 </div>
 <p>The Dratio filter is a powerful tool to identify features that exhibit high variability in the data, relating the variance observed in QC samples with that in study samples. By setting a threshold of 0.4, we remove features that have a high degree of variability between the QC and study samples. In this example, any feature in which the deviation at the QC is higher than 40% (<code>threshold = 0.4</code>)of the deviation on the study samples is removed. This filtering step ensures that only features are retained that have considerably lower technical than biological variance.</p>
 <p>Note that the <code><a href="https://rdrr.io/pkg/MetaboCoreUtils/man/quality_assessment.html" class="external-link">rowDratio()</a></code> and <code><a href="https://rdrr.io/pkg/MetaboCoreUtils/man/quality_assessment.html" class="external-link">rowRsd()</a></code> functions from the <em><a href="https://bioconductor.org/packages/3.20/MetaboCoreUtils" class="external-link">MetaboCoreUtils</a></em> package could be used to calculate the actual numeric values for the estimates used for filtering, to e.g. to evaluate their distribution in the whole data set or identify data set-dependent threshold values.</p>
 <p>Finally, we evaluate the number of features left after the filtering steps and calculate the percentage of features that were removed.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb103"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb107"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Number of features after analysis</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 4277</code></pre>
+<pre><code>[1] 4519</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb105"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb109"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Percentage left: end/beginning</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">/</span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">res_unfilt</span><span class="op">)</span> <span class="op">*</span> <span class="fl">100</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>[1] 47.16586</code></pre>
+<pre><code>[1] 51.79963</code></pre>
 </div>
 </div>
-<p>The dataset has been reduced from 9068 to 4277 features. We did remove a considerable amount of features but this is expected as we want to focus on the most reliable features for our analysis. For the rest of our analysis we need to separate the QC samples from the study samples. We will store the QC samples in a separate object for later use.</p>
+<p>The dataset has been reduced from 8724 to 4519 features. We did remove a considerable amount of features but this is expected as we want to focus on the most reliable features for our analysis. For the rest of our analysis we need to separate the QC samples from the study samples. We will store the QC samples in a separate object for later use.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb107"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb111"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">res_qc</span> <span class="op">&lt;-</span> <span class="va">res</span><span class="op">[</span>, <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">==</span> <span class="st">"QC"</span><span class="op">]</span></span>
 <span><span class="va">res</span> <span class="op">&lt;-</span> <span class="va">res</span><span class="op">[</span>, <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">!=</span> <span class="st">"QC"</span><span class="op">]</span></span></code></pre></div>
 </div>
 <p>We in addition calculate the CV of the QC samples and add that as an additional column to the <code><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData()</a></code> of our result object. These could be used later to prioritize identified <em>significant</em> features with e.g. low technical noise.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb108"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb112"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Calculate the QC's CV and add as feature variable to the data set</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">qc_cv</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res_qc</span>, <span class="st">"norm"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>               <span class="fu"><a href="https://rdrr.io/pkg/MetaboCoreUtils/man/quality_assessment.html" class="external-link">rowRsd</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 </div>
 <p>Now that our data set has been preprocessed, normalized and filtered, we can start to evaluate the distribution of the data and estimate the variation due to biology.</p>
+</section><section class="section level2"><h2 id="overlapping-features">Overlapping features<a class="anchor" aria-label="anchor" href="#overlapping-features"></a>
+</h2>
+<p>a possible evalaution is to which features are overlapping between the different sample groups.</p>
+<p>Fisrt we can plot the overlapping between the CVD and CTR group.</p>
+<p>Before performing complex satistical analysis we can evaluate the data</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb113"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">res_df</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/cbind.html" class="external-link">cbind</a></span><span class="op">(</span>CVD <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MatrixGenerics/man/rowSums.html" class="external-link">rowSums</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res</span>, <span class="st">"raw_filled"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">&lt;</span> <span class="fl">10</span>,</span>
+<span>                 CTR <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/pkg/MatrixGenerics/man/rowSums.html" class="external-link">rowSums</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">res</span>, <span class="st">"raw_filled"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">&lt;</span> <span class="fl">10</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">mat</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+</div>
+<p>In case of a large number of groups a venn diagram can be replace by an upset plot which can handle a larger number of groups, while being still readable. below we compare the overlap between samples.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb114"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#make upsetplot</span></span></code></pre></div>
+</div>
 </section><section class="section level2"><h2 id="differential-abundance-analysis">Differential abundance analysis<a class="anchor" aria-label="anchor" href="#differential-abundance-analysis"></a>
 </h2>
 <p>After normalization and quality control, the next step is to identify features that are differentially abundant between the study groups. This crucial step allows us to identify potential biomarkers or metabolites that are associated with the study groups. There are various approaches and methods available for the identification of such features of interest. In this workflow we use a multiple linear regression analysis to identify features with significantly difference in abundances between the CVD or CTR study group.</p>
 <p>Before performing the tests we evaluate similarities between study samples using a PCA (after excluding the QC samples to avoid them influencing the results).</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb109"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb115"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define the colors for the plot</span></span>
 <span><span class="va">col_sample</span> <span class="op">&lt;-</span> <span class="va">col_phenotype</span><span class="op">[</span><span class="va">res</span><span class="op">$</span><span class="va">phenotype</span><span class="op">]</span></span>
 <span></span>
@@ -2096,22 +2146,22 @@
 <span><span class="va">vals_st</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="va">vals</span>, phenotype <span class="op">=</span> <span class="va">res</span><span class="op">$</span><span class="va">phenotype</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">pca_res</span>, data <span class="op">=</span> <span class="va">vals_st</span> , colour <span class="op">=</span> <span class="st">'phenotype'</span>, scale <span class="op">=</span> <span class="fl">0</span><span class="op">)</span> <span class="op">+</span></span>
 <span>    <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_color_manual</a></span><span class="op">(</span>values <span class="op">=</span> <span class="va">col_phenotype</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-49-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-53-1.png" width="672"></p>
 <figcaption>Figure 34. PCA of the data after normalization and quality control.</figcaption></figure>
 </div>
 </div>
 </div>
 <p>The samples clearly separate by study group on PCA indicating that there are differences in the metabolite profiles between the two groups. However, what drives the separation on PC1 is not clear. Below we evaluate whether this could be explained by the other available variable of our study, i.e., age:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb110"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb116"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Add age to the PCA plot</span></span>
 <span><span class="va">vals_st</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/cbind.html" class="external-link">cbind</a></span><span class="op">(</span><span class="va">vals</span>, age <span class="op">=</span> <span class="va">res</span><span class="op">$</span><span class="va">age</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">pca_res</span>, data <span class="op">=</span> <span class="va">vals_st</span> , colour <span class="op">=</span> <span class="st">'age'</span>, scale <span class="op">=</span> <span class="fl">0</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-50-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-54-1.png" width="672"></p>
 <figcaption>Figure 35. PCA colored by age of the data after normalization and quality control.</figcaption></figure>
 </div>
 </div>
@@ -2120,7 +2170,7 @@
 <p>Even if there is some variance in the data set we can’t explain at this stage, we proceed with the (supervised) statistical tests to identify features of interest. We compute linear models for each metabolite explaining the observed feature abundance by the available study variables. While we could also use the base R function <code><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm()</a></code>, we utilize the <code>R Biocpkg("limma")</code> package to conduct the differential abundance analysis: the <em>moderated</em> test statistics <span class="citation" data-cites="smyth_linear_2004">(<a href="#ref-smyth_linear_2004" role="doc-biblioref">Smyth 2004</a>)</span> provided by this package are specifically well suited for experiments with a limited number of replicates. For our tests we use a linear model <code>~ phenotype + age</code>, hence explaining the abundances of one metabolite accounting for the study group assignment and age of each individual. The analysis might benefit from inclusion of a study covariate associated with PC2 or explaining the variance seen in that principal component, but for the present analysis only the participant’s age and disease association was provided.</p>
 <p>Below we define the design of the study with the <code><a href="https://rdrr.io/r/stats/model.matrix.html" class="external-link">model.matrix()</a></code> function and fit feature-wise linear models to the log2-transformed abundances using the <code><a href="https://rdrr.io/pkg/limma/man/lmFit.html" class="external-link">lmFit()</a></code> function. P-values for significance of association are then calculated using the <code><a href="https://rdrr.io/pkg/limma/man/ebayes.html" class="external-link">eBayes()</a></code> function, that also performs the empirical Bayes-based robust estimation of the standard errors. See also the excellent vignette/user guide of the <em><a href="https://bioconductor.org/packages/3.20/limma" class="external-link">limma</a></em> package for examples and details on the linear model procedure.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb111"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb117"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define the linear model to be applied to the data</span></span>
 <span><span class="va">p.cut</span> <span class="op">&lt;-</span> <span class="fl">0.05</span>     <span class="co"># cut-off for significance.</span></span>
 <span><span class="va">m.cut</span> <span class="op">&lt;-</span> <span class="fl">0.5</span>      <span class="co"># cut-off for log2 fold change</span></span>
@@ -2136,7 +2186,7 @@
 </div>
 <p>After the linear models have been fitted, we can now proceed to extract the results. We create a data frame containing the coefficients, raw and adjusted p-values (applying a Benjamini-Hochberg correction, i.e., <code>method = "BH"</code> for improved control of the false discovery rate), the average intensity of signals in CVD and CTR samples, and an indication of whether a feature is deemed significant or not. We consider all metabolites with an adjusted p-value smaller than 0.05 to be significant, but we could also include the (absolute) difference in abundances in the cut-off criteria. At last, we add the differential abundance results to the result object’s <code><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData()</a></code>.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb112"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb118"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Compile a result data frame</span></span>
 <span><span class="va">tmp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span>
 <span>    coef.CVD <span class="op">=</span> <span class="va">fit</span><span class="op">$</span><span class="va">coefficients</span><span class="op">[</span>, <span class="st">"phenotypeCVD"</span><span class="op">]</span>,</span>
@@ -2153,7 +2203,7 @@
 </div>
 <p>We can now proceed to visualize the distribution of the raw and adjusted p-values.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb113"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb119"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Plot the distribution of p-values</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">pvalue.CVD</span>, breaks <span class="op">=</span> <span class="fl">64</span>, xlab <span class="op">=</span> <span class="st">"p value"</span>,</span>
@@ -2162,7 +2212,7 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/hist.html" class="external-link">hist</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">adjp.CVD</span>, breaks <span class="op">=</span> <span class="fl">64</span>, xlab <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/expression.html" class="external-link">expression</a></span><span class="op">(</span><span class="va">p</span><span class="op">[</span><span class="va">BH</span><span class="op">]</span><span class="op">~</span><span class="va">value</span><span class="op">)</span>,</span>
 <span>     main <span class="op">=</span> <span class="st">"Distribution of adjusted p-values"</span>,</span>
 <span>     cex.main <span class="op">=</span> <span class="fl">1</span>, cex.lab <span class="op">=</span> <span class="fl">1</span>, cex.axis <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/histogram-1.png" width="672"></p>
 <figcaption>Figure 36. Distribution of raw (left) and adjusted p-values (right).</figcaption></figure>
@@ -2172,7 +2222,7 @@
 <p>The histograms above show the distribution of raw and adjusted p-values. Except for an enrichment of small p-values, the raw p-values are (more or less) uniformly distributed, which indicates the absence of any strong systematic biases in the data. The adjusted p-values are more conservative and account for multiple testing; this is important here as we fit a linear model to each feature and therefore perform a large number of tests which leads to a high chance of false positive findings. We do see that some features have very low p-values, indicating that they are likely to be significantly different between the two study groups.</p>
 <p>Below we plot the adjusted p-values against the log2 fold change of (average) abundances. This volcano plot will allow us to visualize the features that are significantly different between the two study groups. These are highlighted with a blue color in the plot below.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb114"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb120"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Plot volcano plot of the statistical results</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>, mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">5</span>, <span class="fl">5</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">coef.CVD</span>, <span class="op">-</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log10</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">adjp.CVD</span><span class="op">)</span>,</span>
@@ -2186,7 +2236,7 @@
 <span>           <span class="op">-</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">log10</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">adjp.CVD</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">significant.CVD</span><span class="op">]</span><span class="op">)</span>,</span>
 <span>           col <span class="op">=</span> <span class="st">"#0000ffcc"</span><span class="op">)</span></span>
 <span><span class="op">}</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
 <figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/volcano-1.png" width="672"></p>
 <figcaption>Figure 37. Volcano plot showing the analysis results.</figcaption></figure>
@@ -2195,7 +2245,7 @@
 </div>
 <p>The most interesting features are in the top corners in the volcano plot (i.e., features with a large difference in abundance between the groups and a small p-value). All significant features have a negative coefficient (log2 fold change value) indicating that their abundance is lower in CVD samples compared to the CTR samples. These features are listed, along with their average difference in (log2) abundance between the compared groups, their adjusted p-values, their average (log2) abundance in each sample group and their RSD (CV) in QC samples in the table below.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb115"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb121"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Table of significant features</span></span>
 <span><span class="va">tab</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">significant.CVD</span>,</span>
 <span>                    <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mzmed"</span>, <span class="st">"rtmed"</span>, <span class="st">"coef.CVD"</span>, <span class="st">"adjp.CVD"</span>,</span>
@@ -2203,7 +2253,7 @@
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="va">tab</span> <span class="op">&lt;-</span> <span class="va">tab</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/order.html" class="external-link">order</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/MathFun.html" class="external-link">abs</a></span><span class="op">(</span><span class="va">tab</span><span class="op">$</span><span class="va">coef.CVD</span><span class="op">)</span>, decreasing <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, <span class="op">]</span></span>
 <span><span class="fu">kable</span><span class="op">(</span><span class="va">tab</span>, format <span class="op">=</span> <span class="st">"pipe"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <table style="width:100%;" class="table">
 <caption>Table 7. Features with significant differences in abundances.</caption>
 <colgroup>
@@ -2231,61 +2281,51 @@
 <td style="text-align: left;">FT0732</td>
 <td style="text-align: right;">182.0749</td>
 <td style="text-align: right;">34.83789</td>
-<td style="text-align: right;">-8.806055</td>
-<td style="text-align: right;">0.0083318</td>
-<td style="text-align: right;">12.226462</td>
-<td style="text-align: right;">3.583991</td>
-<td style="text-align: right;">0.2102895</td>
+<td style="text-align: right;">-8.459808</td>
+<td style="text-align: right;">0.0099281</td>
+<td style="text-align: right;">12.229286</td>
+<td style="text-align: right;">3.859797</td>
+<td style="text-align: right;">0.2116471</td>
 </tr>
 <tr class="even">
 <td style="text-align: left;">FT0845</td>
 <td style="text-align: right;">195.0877</td>
 <td style="text-align: right;">32.65668</td>
-<td style="text-align: right;">-6.334251</td>
-<td style="text-align: right;">0.0308048</td>
-<td style="text-align: right;">16.902516</td>
-<td style="text-align: right;">10.449738</td>
-<td style="text-align: right;">0.0296079</td>
+<td style="text-align: right;">-6.330400</td>
+<td style="text-align: right;">0.0424736</td>
+<td style="text-align: right;">16.905340</td>
+<td style="text-align: right;">10.454994</td>
+<td style="text-align: right;">0.0304712</td>
 </tr>
 <tr class="odd">
 <td style="text-align: left;">FT0565</td>
 <td style="text-align: right;">161.0400</td>
 <td style="text-align: right;">162.13668</td>
-<td style="text-align: right;">-5.624247</td>
-<td style="text-align: right;">0.0261447</td>
-<td style="text-align: right;">10.284250</td>
-<td style="text-align: right;">4.424460</td>
-<td style="text-align: right;">0.0359696</td>
+<td style="text-align: right;">-5.512507</td>
+<td style="text-align: right;">0.0368547</td>
+<td style="text-align: right;">10.287074</td>
+<td style="text-align: right;">4.578369</td>
+<td style="text-align: right;">0.0360903</td>
 </tr>
 <tr class="even">
-<td style="text-align: left;">FT0371</td>
-<td style="text-align: right;">138.0547</td>
-<td style="text-align: right;">148.39599</td>
-<td style="text-align: right;">-5.395260</td>
-<td style="text-align: right;">0.0261447</td>
-<td style="text-align: right;">9.912038</td>
-<td style="text-align: right;">4.141643</td>
-<td style="text-align: right;">0.5555981</td>
-</tr>
-<tr class="odd">
 <td style="text-align: left;">FT1171</td>
 <td style="text-align: right;">229.1299</td>
 <td style="text-align: right;">181.08828</td>
-<td style="text-align: right;">-5.314985</td>
-<td style="text-align: right;">0.0148543</td>
-<td style="text-align: right;">10.718371</td>
-<td style="text-align: right;">5.575420</td>
-<td style="text-align: right;">0.0707526</td>
+<td style="text-align: right;">-5.411058</td>
+<td style="text-align: right;">0.0179212</td>
+<td style="text-align: right;">10.721195</td>
+<td style="text-align: right;">5.500685</td>
+<td style="text-align: right;">0.0706276</td>
 </tr>
-<tr class="even">
-<td style="text-align: left;">FT5606</td>
-<td style="text-align: right;">560.3603</td>
-<td style="text-align: right;">33.54917</td>
-<td style="text-align: right;">-3.682806</td>
-<td style="text-align: right;">0.0262790</td>
-<td style="text-align: right;">8.883094</td>
-<td style="text-align: right;">4.996370</td>
-<td style="text-align: right;">1.2154388</td>
+<tr class="odd">
+<td style="text-align: left;">FT0371</td>
+<td style="text-align: right;">138.0547</td>
+<td style="text-align: right;">148.39599</td>
+<td style="text-align: right;">-5.194885</td>
+<td style="text-align: right;">0.0179212</td>
+<td style="text-align: right;">9.914862</td>
+<td style="text-align: right;">4.405343</td>
+<td style="text-align: right;">0.5564435</td>
 </tr>
 </tbody>
 </table>
@@ -2294,12 +2334,12 @@
 <p>Below we visualize the EICs of the significant features to evaluate their (raw) signal. We restrict the MS data set to study samples. Parameters</p>
 <ul>
 <li>
-<code>keepFeatures = TRUE</code>: ensures that identified features are retained in the `subset object.</li>
+<code>keepFeatures = TRUE</code>: ensures that identified features are retained in the subset object.</li>
 <li>
 <code>peakBg</code>: defines the (background) color for each individual chromatographic peak in the EIC object.</li>
 </ul>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb116"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb122"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Restrict the raw data to study samples.</span></span>
 <span><span class="va">lcms1_study</span> <span class="op">&lt;-</span> <span class="va">lcms1</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span> <span class="op">!=</span> <span class="st">"QC"</span>, keepFeatures <span class="op">=</span> <span class="cn">TRUE</span><span class="op">]</span></span>
 <span><span class="co">#' Extract EICs for the significant features</span></span>
@@ -2312,7 +2352,7 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topright"</span>, col <span class="op">=</span> <span class="va">col_phenotype</span>, legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">col_phenotype</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output-display">
 <div class="quarto-figure quarto-figure-center">
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-53-1.png" width="672"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-57-1.png" width="672"></p>
 <figcaption>Figure 38. Extracted ion chromatograms of the significant features.</figcaption></figure>
 </div>
 </div>
@@ -2326,14 +2366,14 @@
 <p>Our data set was acquired using an LC-MS setup and our features are thus only characterized by their <em>m/z</em> and retention times. The retention time is LC-setup-specific and, without prior data/knowledge provide only little information on the features’ identity. Modern MS instruments have a high accuracy and the <em>m/z</em> values should therefore be reliable estimates of the compound ion’s mass-to-charge ratio. As first approach, we use the features’ <em>m/z</em> values and match them against reference values, i.e., exact masses of chemical compounds that are provided by a reference database, in our case the MassBank database. The full MassBank data is re-distributed through Bioconductor’s <em><a href="https://bioconductor.org/packages/3.20/AnnotationHub" class="external-link">AnnotationHub</a></em> resource, which simplifies their integration into reproducible R-based analysis workflows.</p>
 <p>Below we load the resource, list all available MassBank data sets/releases and load one of them.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb117"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb123"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' load reference data</span></span>
 <span><span class="va">ah</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">AnnotationHub</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="co">#' List available MassBank data sets</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">query</a></span><span class="op">(</span><span class="va">ah</span>, <span class="st">"MassBank"</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>AnnotationHub with 6 records
-# snapshotDate(): 2024-10-24
+# snapshotDate(): 2024-10-28
 # $dataprovider: MassBank
 # $species: NA
 # $rdataclass: CompDb
@@ -2350,13 +2390,13 @@
   AH116165 | MassBank CompDb for release 2023.09
   AH116166 | MassBank CompDb for release 2023.11  </code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb119"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb125"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Load one MAssBank release</span></span>
 <span><span class="va">mb</span> <span class="op">&lt;-</span> <span class="va">ah</span><span class="op">[[</span><span class="st">"AH116166"</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
 </div>
 <p>The MassBank data is provided as a self-contained SQLite database and data can be queried and accessed through the <em><a href="https://bioconductor.org/packages/3.20/CompoundDb" class="external-link">CompoundDb</a></em> Bioconductor package. Below we use the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">compounds()</a></code> function to extract small compound annotations from the database.</p>
 <div class="cell" data-tbl-cap="Table 8. MassBank database extract.">
-<div class="sourceCode cell-code" id="cb120"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb126"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extract compound annotations</span></span>
 <span><span class="va">cmps</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">compounds</a></span><span class="op">(</span><span class="va">mb</span>, columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"formula"</span>,</span>
 <span>                                  <span class="st">"exactmass"</span>, <span class="st">"inchikey"</span><span class="op">)</span><span class="op">)</span></span>
@@ -2373,7 +2413,7 @@
 </div>
 <p>MassBank (as most other small compound annotation databases) provides the (exact) molecular mass of each compound. Since almost all small compounds are neutral in their natural state, they need to be first converted to <em>m/z</em> values to allow matching against the feature’s <em>m/z</em>. To calculate an <em>m/z</em> from a neutral mass, we need to assume which ion (adduct) might be generated from the measured metabolites by the employed electro-spray ionization. In positive polarity, for human serum samples, the most common ions will be protonated (<em>[M+H]+</em>), or bear the addition of sodium (<em>[M+Na]+</em>) or ammonium (<em>[M+H-NH3]+</em>) ions. To match the observed <em>m/z</em> values against reference values from these potential ions we use again the <code><a href="https://rdrr.io/pkg/MetaboAnnotation/man/matchValues.html" class="external-link">matchValues()</a></code> function with the <code>Mass2MzParam</code> approach, that allows to specify the types of expected ions with its <code>adducts</code> parameter and the maximal allowed difference between the compared values using the <code>tolerance</code> and <code>ppm</code> parameters. Below we first prepare the data.frame with the significant features, set up the parameters for the matching and perform the comparison of the query features against the reference database.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb122"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb128"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Prepare query data frame</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">feature_id</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">res_sig</span> <span class="op">&lt;-</span> <span class="va">res</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">rowData</a></span><span class="op">(</span><span class="va">res</span><span class="op">)</span><span class="op">$</span><span class="va">significant.CVD</span>, <span class="op">]</span></span>
@@ -2388,13 +2428,13 @@
 <div class="cell-output cell-output-stdout">
 <pre><code>Object of class Matched
 Total number of matches: 43
-Number of query objects: 6 (4 matched)
+Number of query objects: 5 (4 matched)
 Number of target objects: 25685 (43 matched)</code></pre>
 </div>
 </div>
 <p>The resulting <code>Matched</code> object shows that 4 of our 6 significant features could be matched to ions of compounds from the MassBank database. Below we extract the full result from the <code>Matched</code> object.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb124"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb130"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Extracting the results</span></span>
 <span><span class="va">mtch_res</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">matchedData</a></span><span class="op">(</span><span class="va">mtch</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"feature_id"</span>, <span class="st">"mzmed"</span>, <span class="st">"rtmed"</span>,</span>
 <span>                                <span class="st">"adduct"</span>, <span class="st">"ppm_error"</span>,</span>
@@ -2402,7 +2442,7 @@
 <span>                                <span class="st">"target_inchikey"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">mtch_res</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>DataFrame with 45 rows and 8 columns
+<pre><code>DataFrame with 44 rows and 8 columns
         feature_id     mzmed     rtmed      adduct ppm_error target_formula
        &lt;character&gt; &lt;numeric&gt; &lt;numeric&gt; &lt;character&gt; &lt;numeric&gt;    &lt;character&gt;
 FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2
@@ -2411,11 +2451,11 @@
 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2
 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2
 ...            ...       ...       ...         ...       ...            ...
+FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2
 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1867474      C8H10N4O2
 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.0614704      C8H10N4O2
 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2
 FT1171      FT1171   229.130  181.0883     [M+Na]+ 3.0770838      C12H18N2O
-FT5606      FT5606   560.360   33.5492          NA        NA             NA
          target_name target_inchikey
          &lt;character&gt;     &lt;character&gt;
 FT0371 Benzohydro...   VDEUYMSGMP...
@@ -2424,16 +2464,16 @@
 FT0371 4-Aminoben...   ALYNCZNDIQ...
 FT0371 Anthranili...   RWZYAGGXGH...
 ...              ...             ...
+FT0845      CAFFEINE   RYYVLZVUVI...
 FT0845      Caffeine   RYYVLZVUVI...
 FT0845      caffeine   RYYVLZVUVI...
 FT0845 1,3,7-TRIM...   RYYVLZVUVI...
-FT1171 Isoproturo...   PUIYMUZLKQ...
-FT5606            NA              NA</code></pre>
+FT1171 Isoproturo...   PUIYMUZLKQ...</code></pre>
 </div>
 </div>
 <p>Thus, in total 43 ions of compounds in MassBank were matched to our significant features based on the specified tolerance settings. Many compounds, while having a different structure and thus function/chemical property, have an identical chemical formula and thus mass. Matching exclusively on the <em>m/z</em> of features will hence result in many potentially false positive hits and is thus considered to provide only low confidence annotation. An additional complication is that some annotation resources, like MassBank, being community maintained, contain a large amount of redundant information. To reduce redundancy in the result table we below iterate over the hits of a feature and keep only matches to unique compounds (identified by their INCHIKEY). The INCHI or INCHIKEY combine information from compound’s chemical formula and structure, so while different compounds can share the same chemical formula, they should have a different structure and thus INCHI.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb126"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb132"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">mtch_res</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="cn">NULL</span></span>
 <span></span>
 <span><span class="co">#' Keep only info on features that machted - create a utility function for that</span></span>
@@ -2695,7 +2735,7 @@
 <p>MS2 data can be generated through LC-MS/MS measurement in which MS2 spectra are recorded for ions either in a data dependent acquisition (DDA) or data independent acquisition (DIA) mode. Generally, it is advised to include some LC-MS/MS runs of QC samples or randomly selected study samples already during the acquisition of the MS1 data that is used for quantification of the signals. As an alternative, or in addition, a post-hoc LC-MS/MS acquisition can be performed to generate the MS2 data needed for annotation. For the present experiment, a separate LC-MS/MS measurement was conducted on QC samples and selected study samples to generate such data using an <em>inclusion list</em> of pre-selected ions. These represent features found to be significantly different between CVD and CTR samples in an initial analysis of the full experiment. We use a subset of this second LC-MS/MS data set to show how such data can be used for MS2-based annotation. In our differential abundance analysis we found features with significantly higher abundances in CTR samples. Consequently, we will utilize the MS2 data obtained from the CTR samples to annotate these significant features.</p>
 <p>Below we load the LC-MS/MS data from the experiment and restrict it to data acquired from the CTR sample.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb127"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb133"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Load form the MetaboLights Database</span></span>
 <span><span class="va">param</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MsIO/man/MetaboLightsParam.html" class="external-link">MetaboLightsParam</a></span><span class="op">(</span>mtblsId <span class="op">=</span> <span class="st">"MTBLS8735"</span>,</span>
 <span>                           assayName <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"a_MTBLS8735_LC-MSMS_positive_"</span>,</span>
@@ -2709,7 +2749,7 @@
 <span>                     simplify <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
 </div>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb128"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb134"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#adjust sampleData</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample_name"</span>, <span class="st">"derived_spectra_data_file"</span>,</span>
 <span>                                <span class="st">"metabolite_asssignment_file"</span>,</span>
@@ -2730,7 +2770,7 @@
 <span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"derived_spectra_data_file"</span>,</span>
 <span>                     <span class="st">"phenotype"</span>, <span class="st">"sample_name"</span>, <span class="st">"age"</span><span class="op">)</span><span class="op">]</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu">kable</span><span class="op">(</span>format <span class="op">=</span> <span class="st">"pipe"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <table class="table">
 <caption>Table 10. Samples from the LC-MS/MS data set.</caption>
 <thead><tr class="header">
@@ -2761,13 +2801,14 @@
 </tbody>
 </table>
 </div>
-<div class="sourceCode cell-code" id="cb129"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb135"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Filter the data to the same RT range as the LC-MS run</span></span>
 <span><span class="va">lcms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterRt</a></span><span class="op">(</span><span class="va">lcms2</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">10</span>, <span class="fl">240</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+</details>
 </div>
 <p>In total we have 3 LC-MS/MS data files for the control samples, each with a different collision energy to fragment the ions. Below we show the number of MS1 and MS2 spectra for each of the files.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb130"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb136"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' check the number of spectra per ms level</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectra</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">msLevel</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -2782,11 +2823,11 @@
 </div>
 <p>Compared to the number of MS2 spectra, far less MS1 spectra were acquired. The configuration of the MS instrument was set to ensure that the ions specified in the <em>inclusion list</em> were selected for fragmentation, even if their intensity might be very low. With this setting, however, most of the recorded MS2 spectra will represent only noise. The plot below shows the location of precursor ions in the <em>m/z</em> - retention time plane for the three files.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb132"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb138"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/xcms/man/plotPrecursorIons.html" class="external-link">plotPrecursorIons</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output-display">
 <div>
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-62-1.png" width="480"></p>
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-66-1.png" width="480"></p>
 </figure>
 </div>
 </div>
@@ -2794,14 +2835,14 @@
 <p>We can see MS2 spectra being recorded for the <em>m/z</em> of interest along the full retention time range, even if the actual ions will only be eluting within certain retention time windows.</p>
 <p>We next extract the <code>Spectra</code> object with the MS data from the data object and assign a new spectra variable with the employed collision energy, which we extract from the data object <code>sampleData</code>.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb133"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb139"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">ms2_ctr</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectra</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span></span>
 <span><span class="va">ms2_ctr</span><span class="op">$</span><span class="va">collision_energy</span> <span class="op">&lt;-</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span><span class="op">$</span><span class="va">fragmentation_mode</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">spectraSampleIndex</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span><span class="op">]</span></span></code></pre></div>
 </div>
 <p>We next filter the MS data by first restricting to MS2 spectra and then removing mass peaks from each spectrum with an intensity that is lower than 5% of the highest intensity for that spectrum, assuming that these very low intensity peaks represent background signal.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb134"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb140"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Remove low intensity peaks</span></span>
 <span><span class="va">low_int</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span>, <span class="va">...</span><span class="op">)</span> <span class="op">{</span></span>
 <span>    <span class="va">x</span> <span class="op">&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">x</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">*</span> <span class="fl">0.05</span></span>
@@ -2812,7 +2853,7 @@
 </div>
 <p>We next remove also mass peaks with an <em>m/z</em> value greater or equal to the precursor <em>m/z</em> of the ion. This puts, for the later matching against reference spectra, more weight on the fragmentation pattern of ions and avoids hits based only on the precursor <em>m/z</em> peak (and hence a similar mass of the compared compounds). At last, we restrict the data to spectra with at least two fragment peaks and scale their intensities such that their sum is 1 for each spectrum. While similarity calculations are not affected by this scaling, it makes visual comparison of fragment spectra easier to read.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb135"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb141"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Remove precursor peaks and restrict to spectra with a minimum</span></span>
 <span><span class="co">#' number of peaks</span></span>
 <span><span class="va">ms2_ctr</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/filterMsLevel.html" class="external-link">filterPrecursorPeaks</a></span><span class="op">(</span><span class="va">ms2_ctr</span>, ppm <span class="op">=</span> <span class="fl">50</span>, mz <span class="op">=</span> <span class="st">"&gt;="</span><span class="op">)</span></span>
@@ -2821,13 +2862,13 @@
 </div>
 <p>Finally, also to speed up the later comparison of the spectra against the reference database, we load the full MS data into memory (by changing the <em>backend</em> to <code>MsBackendMemory</code>) and <em>apply</em> all processing steps performed on this data so far. Keeping the MS data in memory has performance benefits, but is generally not suggested for large data sets. To evaluate the impact for the present data set we print in addition the size of the data object before and after changing the backend.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb136"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb142"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Size of the object before loading into memory</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/utils/object.size.html" class="external-link">object.size</a></span><span class="op">(</span><span class="va">ms2_ctr</span><span class="op">)</span>, units <span class="op">=</span> <span class="st">"MB"</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>5.1 Mb</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb138"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb144"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Load the MS data subset into memory</span></span>
 <span><span class="va">ms2_ctr</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/backendInitialize.html" class="external-link">setBackend</a></span><span class="op">(</span><span class="va">ms2_ctr</span>, <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/MsBackend.html" class="external-link">MsBackendMemory</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="va">ms2_ctr</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/addProcessing.html" class="external-link">applyProcessing</a></span><span class="op">(</span><span class="va">ms2_ctr</span><span class="op">)</span></span>
@@ -2842,7 +2883,7 @@
 <p>While we could proceed and match all these experimental MS2 spectra against reference fragment spectra, for our workflow we aim to annotate the features found significant in the differential abundance analysis. The goal is thus to identify the MS2 spectra from the second (LC-MS/MS) run that could represent fragments of the ions of features from the data in the first (LC-MS) run. Our approach is to match MS2 spectra against the significant features determined earlier based on their precursor <em>m/z</em> and retention time (given an acceptable tolerance) to the feature’s <em>m/z</em> and retention time. This can be easily done using the <code><a href="https://rdrr.io/pkg/xcms/man/XcmsExperiment.html" class="external-link">featureArea()</a></code> function that effectively considers the actual <em>m/z</em> and retention time ranges of the features’ chromatographic peaks and therefore increase the chance of finding a correct match. This however also assumes that retention times between the first and second run don’t differ by much. Alternatively, we would need to align the retention times of the second LC-MS/MS data set to those of the first.</p>
 <p>Below we first extract the <em>feature area</em>, i.e., the <em>m/z</em> and retention time ranges, for the significant features.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb140"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb146"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Define the m/z and retention time ranges for the significant features</span></span>
 <span><span class="va">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XcmsExperiment.html" class="external-link">featureArea</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">res_sig</span><span class="op">)</span>, <span class="op">]</span></span>
 <span><span class="va">target</span></span></code></pre></div>
@@ -2852,13 +2893,12 @@
 FT0565 161.0391 161.0407 159.00234 164.30799
 FT0732 182.0726 182.0756  32.71242  42.28755
 FT0845 195.0799 195.0887  30.73235  35.67337
-FT1171 229.1282 229.1335 178.01450 183.35303
-FT5606 560.3539 560.3656  32.06570  35.33456</code></pre>
+FT1171 229.1282 229.1335 178.01450 183.35303</code></pre>
 </div>
 </div>
 <p>We next identify the fragment spectra with their precursor <em>m/z</em> and retention times within these ranges. We use the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterRanges()</a></code> function that allows to filter a <code>Spectra</code> object using multiple ranges simultaneously. We apply this function separately to each feature (row in the above matrix) to extract the MS2 spectra representing fragmentation information of the presumed feature’s ions.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb142"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb148"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Identify for each feature MS2 spectra with their precursor m/z and</span></span>
 <span><span class="co">#' retention time within the feature's m/z and retention time range</span></span>
 <span><span class="va">ms2_ctr_fts</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/apply.html" class="external-link">apply</a></span><span class="op">(</span><span class="va">target</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtmin"</span>, <span class="st">"rtmax"</span>, <span class="st">"mzmin"</span>, <span class="st">"mzmax"</span><span class="op">)</span><span class="op">]</span>,</span>
@@ -2866,13 +2906,13 @@
 <span>                     spectraVariables <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"rtime"</span>, <span class="st">"precursorMz"</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/lengths.html" class="external-link">lengths</a></span><span class="op">(</span><span class="va">ms2_ctr_fts</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>FT0371 FT0565 FT0732 FT0845 FT1171 FT5606
-    38     36    135     68     38      0 </code></pre>
+<pre><code>FT0371 FT0565 FT0732 FT0845 FT1171
+    38     36    135     68     38 </code></pre>
 </div>
 </div>
 <p>The result from this <code><a href="https://rdrr.io/r/base/apply.html" class="external-link">apply()</a></code> call is a <code>list</code> of <code>Spectra</code>, each element representing the result for one feature. With the exception of the last feature, multiple MS2 spectra could be identified. We next combine the <code>list</code> of <code>Spectra</code> into a single <code>Spectra</code> object using the <code><a href="https://rdrr.io/pkg/Spectra/man/combineSpectra.html" class="external-link">concatenateSpectra()</a></code> function and add an additional spectra variable containing the respective feature identifier.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb144"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb150"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lengths.html" class="external-link">lengths</a></span><span class="op">(</span><span class="va">ms2_ctr_fts</span><span class="op">)</span></span>
 <span><span class="co">#' Combine the individual Spectra objects</span></span>
 <span><span class="va">ms2_ctr_fts</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/combineSpectra.html" class="external-link">concatenateSpectra</a></span><span class="op">(</span><span class="va">ms2_ctr_fts</span><span class="op">)</span></span>
@@ -2882,7 +2922,7 @@
 <p>We have now a <code>Spectra</code> object with fragment spectra for the significant features from our differential expression analysis.</p>
 <p>We next build our reference data which we need to process the same way as our <em>query</em> spectra. We extract all fragment spectra from the MassBank database, restrict to positive polarity data (since our experiment was acquired in positive polarity) and perform the same processing to the fragment spectra from the MassBank database.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb145"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb151"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">ms2_ref</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/Spectra.html" class="external-link">Spectra</a></span><span class="op">(</span><span class="va">mb</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterPolarity</a></span><span class="op">(</span><span class="fl">1L</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterIntensity</a></span><span class="op">(</span>intensity <span class="op">=</span> <span class="va">low_int</span><span class="op">)</span> <span class="op">|&gt;</span></span>
@@ -2901,7 +2941,7 @@
 <code>THRESHFUN</code>: Defines which matches to report. Here, we keep all matches resulting in a spectra similarity score (calculated with the normalized dot product <span class="citation" data-cites="stein_optimization_1994">(<a href="#ref-stein_optimization_1994" role="doc-biblioref">Stein and Scott 1994</a>)</span>, the default similarity function) larger than 0.6.</li>
 </ul>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb146"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb152"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/BiocParallel/man/register.html" class="external-link">register</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/BiocParallel/man/SerialParam-class.html" class="external-link">SerialParam</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#' Define the settings for the spectra matching.</span></span>
 <span><span class="va">prm</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/CompareSpectraParam.html" class="external-link">CompareSpectraParam</a></span><span class="op">(</span>ppm <span class="op">=</span> <span class="fl">40</span>, tolerance <span class="op">=</span> <span class="fl">0.05</span>,</span>
@@ -2920,7 +2960,7 @@
 <p>Thus, from the total 315 query MS2 spectra, only 16 could be matched to (at least) one reference fragment spectrum.</p>
 <p>Below we restrict the results to these matching spectra and extract all metadata of the query and target spectra as well as the similarity score (the complete list of available metadata information can be listed with the <code><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">colnames()</a></code> function).</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb148"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb154"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Keep only query spectra with matching reference spectra</span></span>
 <span><span class="va">ms2_mtch</span> <span class="op">&lt;-</span> <span class="va">ms2_mtch</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/MetaboAnnotation/man/Matched.html" class="external-link">whichQuery</a></span><span class="op">(</span><span class="va">ms2_mtch</span><span class="op">)</span><span class="op">]</span></span>
 <span></span>
@@ -2932,7 +2972,7 @@
 </div>
 </div>
 <p>Now, we have query-target pairs with a spectra similarity higher than 0.6. Similar to the MS1-based annotation also this result table contains redundant information: we have multiple fragment spectra per feature and also MassBank contains several fragment spectra for each compound, measured using differing collision energies or MS instruments, by different laboratories. Below we thus iterate over the feature-compound pairs and select for each the one with the highest score. As an identifier for a compound, we use its fragment spectra’s INCHI-key, since compound names in MassBank do not have accepted consensus/controlled vocabularies.</p>
-<div class="sourceCode cell-code" id="cb150"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb156"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' - split the result per feature</span></span>
 <span><span class="co">#' - select for each feature the best matching result for each compound</span></span>
 <span><span class="co">#' - combine the result again into a data frame</span></span>
@@ -2998,11 +3038,11 @@
 </tr>
 </tbody>
 </table>
-<p>Thus, from the 6 significant features, only one could be annotated to a compound based on the MS2-based approach. There could be many reasons for the failure to find matches for the other features. Although MS2 spectra were selected for each feature, most appear to only represent noise, as for most features, in the LC-MS/MS run, no or only very low MS1 signal was recorded, indicating that in that selected sample the original compound might not (or no longer) be present. Also, most reference databases contain predominantly fragment spectra for protonated (<em>[M+H]+</em>) ions of compounds, while some of the features might represent signal from other types of ions which would result in a different fragmentation pattern. Finally, fragment spectra of compounds of interest might also simply not be present in the used reference database.</p>
+<p>Thus, from the 5 significant features, only one could be annotated to a compound based on the MS2-based approach. There could be many reasons for the failure to find matches for the other features. Although MS2 spectra were selected for each feature, most appear to only represent noise, as for most features, in the LC-MS/MS run, no or only very low MS1 signal was recorded, indicating that in that selected sample the original compound might not (or no longer) be present. Also, most reference databases contain predominantly fragment spectra for protonated (<em>[M+H]+</em>) ions of compounds, while some of the features might represent signal from other types of ions which would result in a different fragmentation pattern. Finally, fragment spectra of compounds of interest might also simply not be present in the used reference database.</p>
 <p>Thus, combining the information from the MS1- and MS2 based annotation we can only annotate one feature with a considerable confidence. The feature with the <em>m/z</em> of 195.0879 and a retention time of 32 seconds seems to be an ion of caffeine. While the result is somewhat disappointing it also clearly shows the importance of proper experimental planning and the need to control for all potential confounding factors. For the present experiment, no disease-specific biomarker could be identified, but a life-style property of individuals suffering from this disease: coffee consumption was probably contraindicated to patients of the CVD group to reduce the risk of heart arrhythmia.</p>
 <p>We below plot the EIC for this feature highlighting the retention time at which the highest scoring MS2 spectra was recorded and create a mirror plot comparing this MS2 spectra to the reference fragment spectra for caffeine.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb151"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb157"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">col_sample</span> <span class="op">&lt;-</span> <span class="va">col_phenotype</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span><span class="op">]</span></span>
 <span><span class="co">#' Extract and plot EIC for the annotated feature</span></span>
 <span><span class="va">eic</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/featureChromatograms.html" class="external-link">featureChromatograms</a></span><span class="op">(</span><span class="va">lcms1</span>, features <span class="op">=</span> <span class="va">ms2_mtch_res</span><span class="op">$</span><span class="va">feature_id</span><span class="op">[</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span>
@@ -3015,13 +3055,15 @@
 <span></span>
 <span><span class="co">#' Indicate the retention time of the MS2 spectrum in the EIC plot</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span>v <span class="op">=</span> <span class="va">ms2_mtch_res</span><span class="op">$</span><span class="va">rtime</span><span class="op">[</span><span class="va">idx</span><span class="op">]</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
-<div>
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-73-1.png" width="480"></p>
-</figure>
+</details><div class="cell-output-display">
+<div class="quarto-figure quarto-figure-center">
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-77-1.png" width="480"></p>
+<figcaption>Figure 39. Extracted ion chromatograms and MS2 spectra for the annotated feature.</figcaption></figure>
 </div>
 </div>
-<div class="sourceCode cell-code" id="cb152"><pre class="downlit sourceCode r">
+</div>
+<div class="cell">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb158"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Get the index of the MS2 spectrum in the query object</span></span>
 <span><span class="va">query_idx</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/AnnotationHub/man/AnnotationHub-class.html" class="external-link">query</a></span><span class="op">(</span><span class="va">ms2_mtch</span><span class="op">)</span><span class="op">$</span><span class="va">.original_query_index</span> <span class="op">==</span></span>
 <span>                                  <span class="va">ms2_mtch_res</span><span class="op">$</span><span class="va">.original_query_index</span><span class="op">[</span><span class="va">idx</span><span class="op">]</span><span class="op">)</span></span>
@@ -3035,17 +3077,17 @@
 <span><span class="fu"><a href="https://rdrr.io/pkg/Spectra/man/spectra-plotting.html" class="external-link">plotSpectraMirror</a></span><span class="op">(</span><span class="va">query_ms2</span>, <span class="va">target_ms2</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topleft"</span>,</span>
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"precursor m/z: "</span>, <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/format.html" class="external-link">format</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">precursorMz</a></span><span class="op">(</span><span class="va">query_ms2</span><span class="op">)</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
-<div>
-<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-73-2.png" width="480"></p>
-</figure>
+</details><div class="cell-output-display">
+<div class="quarto-figure quarto-figure-center">
+<figure><p><img src="a-end-to-end-untargeted-metabolomics_files/figure-html/unnamed-chunk-78-1.png" width="672"></p>
+<figcaption>Figure 40. MS2 spectra for the annotated feature.</figcaption></figure>
 </div>
 </div>
 </div>
 <p>The plot above clearly shows the higher signal for this feature in CTR compared to CVD samples. The QC samples exhibit a lower but highly consistent signal, suggesting the absence of strong technical noise or biases in the raw data of this experiment. The vertical line indicates the retention time of the fragment spectrum with the best match against a reference spectrum. It has to be noted that, since the fragment spectra were measured in a separate LC-MS/MS experiment, this should only be considered as an indication of the approximate retention time in which ions were fragmented in the second experiment. The fragment spectrum for this feature, shown in the upper panel of the right plot above is highly similar to the reference spectrum for caffeine from MassBank (shown in the lower panel). In addition to their matching precursor <em>m/z</em>, the same two fragments (same <em>m/z</em> and intensity) are present in both spectra.</p>
 <p>We can also extract additional metadata for the matching reference spectrum, such as the used collision energy, fragmentation mode, instrument type, instrument as well as the ion (adduct) that was fragmented.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb153"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb159"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">spectraData</a></span><span class="op">(</span><span class="va">target_ms2</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"collisionEnergy_text"</span>, <span class="st">"fragmentation_mode"</span>,</span>
 <span>                          <span class="st">"instrument_type"</span>, <span class="st">"instrument"</span>, <span class="st">"adduct"</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/as.data.frame.html" class="external-link">as.data.frame</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
@@ -3068,16 +3110,16 @@
 </section><section class="section level2"><h2 id="session-information">Session information<a class="anchor" aria-label="anchor" href="#session-information"></a>
 </h2>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb155"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb161"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code>R version 4.4.1 (2024-06-14)
+<pre><code>R version 4.4.2 (2024-10-31)
 Platform: x86_64-pc-linux-gnu
-Running under: Ubuntu 22.04.5 LTS
+Running under: Ubuntu 24.04.1 LTS
 
 Matrix products: default
 BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
+LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
 
 locale:
  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
@@ -3095,97 +3137,95 @@
 [8] base
 
 other attached packages:
- [1] MetaboAnnotation_1.9.2       CompoundDb_1.9.5
- [3] AnnotationFilter_1.29.0      AnnotationHub_3.13.3
- [5] BiocFileCache_2.13.2         dbplyr_2.5.0
- [7] gridExtra_2.3                ggfortify_0.4.17
- [9] ggplot2_3.5.1                vioplot_0.5.0
-[11] zoo_1.8-12                   sm_2.2-6.0
-[13] pheatmap_1.0.12              RColorBrewer_1.1-3
-[15] pander_0.6.5                 limma_3.61.12
-[17] MetaboCoreUtils_1.13.0       xcms_4.5.0
-[19] MsBackendMetaboLights_0.99.1 Spectra_1.15.13
-[21] BiocParallel_1.39.0          alabaster.se_1.5.4
-[23] alabaster.base_1.5.10        SummarizedExperiment_1.35.5
-[25] Biobase_2.65.1               GenomicRanges_1.57.2
-[27] GenomeInfoDb_1.41.2          IRanges_2.39.2
-[29] S4Vectors_0.43.2             BiocGenerics_0.51.3
-[31] MatrixGenerics_1.17.1        matrixStats_1.4.1
-[33] MsIO_0.0.8                   MsExperiment_1.7.0
-[35] ProtGenerics_1.37.1          readxl_1.4.3
-[37] BiocStyle_2.33.1             quarto_1.4.4
-[39] knitr_1.48
+ [1] MetaboAnnotation_1.10.1     CompoundDb_1.10.0
+ [3] AnnotationFilter_1.30.0     AnnotationHub_3.14.0
+ [5] BiocFileCache_2.14.0        dbplyr_2.5.0
+ [7] gridExtra_2.3               ggfortify_0.4.17
+ [9] ggplot2_3.5.1               vioplot_0.5.0
+[11] zoo_1.8-12                  sm_2.2-6.0
+[13] pheatmap_1.0.12             RColorBrewer_1.1-3
+[15] pander_0.6.5                limma_3.62.1
+[17] MetaboCoreUtils_1.14.0      xcms_4.5.1
+[19] MsBackendMetaboLights_1.0.0 Spectra_1.16.0
+[21] BiocParallel_1.40.0         alabaster.se_1.6.0
+[23] alabaster.base_1.6.1        SummarizedExperiment_1.36.0
+[25] Biobase_2.66.0              GenomicRanges_1.58.0
+[27] GenomeInfoDb_1.42.1         IRanges_2.40.0
+[29] S4Vectors_0.44.0            BiocGenerics_0.52.0
+[31] MatrixGenerics_1.18.0       matrixStats_1.4.1
+[33] MsIO_0.0.8                  MsExperiment_1.8.0
+[35] ProtGenerics_1.38.0         readxl_1.4.3
+[37] BiocStyle_2.34.0            quarto_1.4.4
+[39] knitr_1.49
 
 loaded via a namespace (and not attached):
-  [1] later_1.3.2                 bitops_1.0-9
+  [1] later_1.4.1                 bitops_1.0-9
   [3] filelock_1.0.3              tibble_3.2.1
-  [5] cellranger_1.1.0            preprocessCore_1.67.1
+  [5] cellranger_1.1.0            preprocessCore_1.68.0
   [7] XML_3.99-0.17               lifecycle_1.0.4
   [9] doParallel_1.0.17           processx_3.8.4
  [11] lattice_0.22-6              MASS_7.3-61
- [13] MultiAssayExperiment_1.31.5 magrittr_2.0.3
- [15] rmarkdown_2.28              yaml_2.3.10
- [17] MsCoreUtils_1.17.3          DBI_1.2.3
- [19] abind_1.4-8                 zlibbioc_1.51.2
+ [13] MultiAssayExperiment_1.32.0 magrittr_2.0.3
+ [15] rmarkdown_2.29              yaml_2.3.10
+ [17] MsCoreUtils_1.18.0          DBI_1.2.3
+ [19] abind_1.4-8                 zlibbioc_1.52.0
  [21] purrr_1.0.2                 RCurl_1.98-1.16
  [23] rappdirs_0.3.3              GenomeInfoDbData_1.2.13
- [25] MSnbase_2.31.1              ncdf4_1.23
- [27] codetools_0.2-20            DelayedArray_0.31.14
+ [25] MSnbase_2.32.0              ncdf4_1.23
+ [27] codetools_0.2-20            DelayedArray_0.32.0
  [29] xml2_1.3.6                  DT_0.33
  [31] tidyselect_1.2.1            farver_2.1.2
- [33] UCSC.utils_1.1.0            base64enc_0.1-3
+ [33] UCSC.utils_1.2.0            base64enc_0.1-3
  [35] jsonlite_1.8.9              iterators_1.0.14
- [37] foreach_1.5.2               tools_4.4.1
- [39] progress_1.2.3              Rcpp_1.0.13
- [41] glue_1.8.0                  SparseArray_1.5.45
- [43] xfun_0.48                   dplyr_1.1.4
- [45] HDF5Array_1.33.8            withr_3.0.2
+ [37] foreach_1.5.2               tools_4.4.2
+ [39] progress_1.2.3              Rcpp_1.0.13-1
+ [41] glue_1.8.0                  SparseArray_1.6.0
+ [43] xfun_0.49                   dplyr_1.1.4
+ [45] HDF5Array_1.34.0            withr_3.0.2
  [47] BiocManager_1.30.25         fastmap_1.2.0
- [49] rhdf5filters_1.17.0         fansi_1.0.6
+ [49] rhdf5filters_1.18.0         fansi_1.0.6
  [51] digest_0.6.37               mime_0.12
  [53] R6_2.5.1                    colorspace_2.1-1
- [55] rsvg_2.6.1                  RSQLite_2.3.7
+ [55] rsvg_2.6.1                  RSQLite_2.3.8
  [57] utf8_1.2.4                  tidyr_1.3.1
  [59] generics_0.1.3              prettyunits_1.2.0
- [61] PSMatch_1.9.1               httr_1.4.7
- [63] htmlwidgets_1.6.4           S4Arrays_1.5.11
+ [61] PSMatch_1.10.0              httr_1.4.7
+ [63] htmlwidgets_1.6.4           S4Arrays_1.6.0
  [65] pkgconfig_2.0.3             gtable_0.3.6
- [67] blob_1.2.4                  impute_1.79.0
- [69] MassSpecWavelet_1.71.0      XVector_0.45.0
+ [67] blob_1.2.4                  impute_1.80.0
+ [69] MassSpecWavelet_1.72.0      XVector_0.46.0
  [71] htmltools_0.5.8.1           MALDIquant_1.22.3
- [73] clue_0.3-65                 scales_1.3.0
- [75] alabaster.matrix_1.5.10     png_0.1-8
+ [73] clue_0.3-66                 scales_1.3.0
+ [75] alabaster.matrix_1.6.1      png_0.1-8
  [77] rstudioapi_0.17.1           reshape2_1.4.4
- [79] rjson_0.2.23                curl_5.2.3
- [81] cachem_1.1.0                rhdf5_2.49.0
+ [79] rjson_0.2.23                curl_6.0.1
+ [81] cachem_1.1.0                rhdf5_2.50.0
  [83] stringr_1.5.1               BiocVersion_3.20.0
- [85] parallel_4.4.1              AnnotationDbi_1.67.0
- [87] mzID_1.43.0                 vsn_3.73.0
- [89] pillar_1.9.0                grid_4.4.1
- [91] alabaster.schemas_1.5.0     vctrs_0.6.5
- [93] MsFeatures_1.13.0           pcaMethods_1.97.0
+ [85] parallel_4.4.2              AnnotationDbi_1.68.0
+ [87] mzID_1.44.0                 vsn_3.74.0
+ [89] pillar_1.9.0                grid_4.4.2
+ [91] alabaster.schemas_1.6.0     vctrs_0.6.5
+ [93] MsFeatures_1.14.0           pcaMethods_1.98.0
  [95] cluster_2.1.6               evaluate_1.0.1
- [97] cli_3.6.3                   compiler_4.4.1
+ [97] cli_3.6.3                   compiler_4.4.2
  [99] rlang_1.1.4                 crayon_1.5.3
-[101] labeling_0.4.3              QFeatures_1.15.3
-[103] ChemmineR_3.57.1            ps_1.8.1
-[105] affy_1.83.1                 plyr_1.8.9
-[107] fs_1.6.4                    stringi_1.8.4
-[109] munsell_0.5.1               Biostrings_2.73.2
+[101] labeling_0.4.3              QFeatures_1.16.0
+[103] ChemmineR_3.58.0            ps_1.8.1
+[105] affy_1.84.0                 plyr_1.8.9
+[107] fs_1.6.5                    stringi_1.8.4
+[109] munsell_0.5.1               Biostrings_2.74.0
 [111] lazyeval_0.2.2              Matrix_1.7-1
 [113] hms_1.1.3                   bit64_4.5.2
-[115] Rhdf5lib_1.27.0             KEGGREST_1.45.1
-[117] statmod_1.5.0               alabaster.ranges_1.5.2
-[119] mzR_2.39.2                  igraph_2.1.1
-[121] memoise_2.0.1               affyio_1.75.1
+[115] Rhdf5lib_1.28.0             KEGGREST_1.46.0
+[117] statmod_1.5.0               alabaster.ranges_1.6.0
+[119] mzR_2.40.0                  igraph_2.1.1
+[121] memoise_2.0.1               affyio_1.76.0
 [123] bit_4.5.0                  </code></pre>
 </div>
 </div>
 </section><section class="section level2"><h2 id="aknowledgment">Aknowledgment<a class="anchor" aria-label="anchor" href="#aknowledgment"></a>
 </h2>
 <p>Thanks to Steffen Neumann for his continuous work to develop and maintain the xcms software…</p>
-</section><section class="section level2"><h2 id="additional-informations">Additional informations<a class="anchor" aria-label="anchor" href="#additional-informations"></a>
-</h2>
 </section><section class="section level2"><h2 class="unnumbered" id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
 </h2>
 <div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
diff --git a/articles/a-end-to-end-untargeted-metabolomics_files/figure-html/Detected-vs-filled-signal1-1.png b/articles/a-end-to-end-untargeted-metabolomics_files/figure-html/Detected-vs-filled-signal1-1.png
index 2dff4590ae7783b83d3c957343b5b2aaf2ae2195..366d107d7eff90080e98b9f10b56e2312227896a 100644
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diff --git a/articles/alignment-to-external-dataset.html b/articles/alignment-to-external-dataset.html
index 9b929c2..756bd9a 100644
--- a/articles/alignment-to-external-dataset.html
+++ b/articles/alignment-to-external-dataset.html
@@ -14,8 +14,8 @@
 <link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><script src="alignment-to-external-dataset_files/libs/quarto-html/popper.min.js"></script><script src="alignment-to-external-dataset_files/libs/quarto-html/tippy.umd.min.js"></script><link href="alignment-to-external-dataset_files/libs/quarto-html/tippy.css" rel="stylesheet">
 <link href="alignment-to-external-dataset_files/libs/quarto-html/light-border.css" rel="stylesheet">
 <!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset">
@@ -29,7 +29,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -62,10 +62,10 @@
 </nav><div class="container template-quarto">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.png" class="logo" alt=""><h1>Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset</h1>
+      <img src="../logo.png" class="logo" alt=""><h1></h1>
 
 
-      <p class="date">2024-10-30</p>
+      <p class="date">2024-12-04</p>
 
       <small class="dont-index">Source: <a href="https://github.com/rformassspectrometry/metabonaut/blob/main/vignettes/alignment-to-external-dataset.qmd" class="external-link"><code>vignettes/alignment-to-external-dataset.qmd</code></a></small>
       <div class="d-none name"><code></code></div>
@@ -76,7 +76,14 @@
 
 
 
-    <section class="section level2"><h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
+    <header id="title-block-header" class="quarto-title-block"><h1 class="title">Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset</h1></header><div class="cell">
+<style type="text/css">
+.verysmall .table{
+  font-size: 8px;
+}
+</style>
+</div>
+<section class="section level2"><h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
 </h2>
 <p>In certain experiments, aligning datasets recorded at different times is necessary. This can involve comparing runs of the same samples from different laboratories or matching MS2 data to an initial MS1 run. Variation in retention time across laboratories and LC systems often requires an alignment step using <code><a href="https://rdrr.io/pkg/xcms/man/adjustRtime.html" class="external-link">adjustRtime()</a></code> with the <code>LamaParama</code> parameter.</p>
 <p>As described in the <a href="https://rformassspectrometry.github.io/metabonaut/articles/dataset-investigation.html">data description</a> vignette, some of our samples were run twice: once in LC-MS mode and again in LC-MS/MS mode. This tutorial will show how to align the LC-MS/MS run to the preprocessed LC-MS dataset.</p>
@@ -110,42 +117,12 @@
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XcmsExperiment.html" class="external-link">XcmsExperiment</a></span><span class="op">(</span><span class="op">)</span>,</span>
 <span>    <span class="fu"><a href="https://rdrr.io/pkg/MsIO/man/AlabasterParam.html" class="external-link">AlabasterParam</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/system.file.html" class="external-link">system.file</a></span><span class="op">(</span><span class="st">"extdata"</span>, <span class="st">"preprocessed_lcms1"</span>,</span>
 <span>                               package <span class="op">=</span> <span class="st">"metabonaut"</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_1_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_A_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_B_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_2_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_C_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_D_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_3_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_E_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_F_POS.mzML'</code></pre>
-</div>
-<div class="cell-output cell-output-stderr">
-<pre><code>adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_4_POS.mzML'</code></pre>
-</div>
 </div>
 </section><section class="level2"><h3 id="load-unprocessed-lc-msms-data">Load unprocessed LC-MS/MS data<a class="anchor" aria-label="anchor" href="#load-unprocessed-lc-msms-data"></a>
 </h3>
 <p>Next, we will load the unprocessed LC-MS/MS data from the MetaboLights database:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Load form the MetaboLights Database</span></span>
 <span><span class="va">mlp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MsIO/man/MetaboLightsParam.html" class="external-link">MetaboLightsParam</a></span><span class="op">(</span>mtblsId <span class="op">=</span> <span class="st">"MTBLS8735"</span>,</span>
 <span>                         assayName <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="st">"a_MTBLS8735_LC-MSMS_positive_"</span>,</span>
@@ -160,7 +137,7 @@
 </div>
 <p>We will adjust the <code><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData()</a></code> of the LC-MS/MS object to make it easier to access:</p>
 <div class="cell">
-<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb15"><pre class="downlit sourceCode r">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#adjust sampleData</span></span>
 <span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/row_colnames.html" class="external-link">colnames</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample_name"</span>, <span class="st">"derived_spectra_data_file"</span>,</span>
 <span>                                <span class="st">"metabolite_asssignment_file"</span>,</span>
@@ -175,7 +152,7 @@
 <span>    <span class="fu">kable</span><span class="op">(</span>format <span class="op">=</span> <span class="st">"pipe"</span><span class="op">)</span></span></code></pre></div>
 </details><div class="cell-output-display">
 <table class="table caption-top">
-<caption>Table 10. Samples from the LC-MS/MS data set.</caption>
+<caption>Table 1. Samples from the LC-MS/MS data set.</caption>
 <thead><tr class="header">
 <th style="text-align: left;">derived_spectra_data_file</th>
 <th style="text-align: left;">phenotype</th>
@@ -261,18 +238,18 @@
 </div>
 <p>We will only keep the MS runs (not MS/MS) and remove pooled samples, focusing on samples <em>A</em> and <em>E</em> that are common to both runs.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Only keep MS run</span></span>
 <span><span class="va">lcms2</span> <span class="op">&lt;-</span> <span class="va">lcms2</span><span class="op">[</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/grep.html" class="external-link">grepl</a></span><span class="op">(</span><span class="st">"MSMS"</span>, <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span><span class="op">$</span><span class="va">derived_spectra_data_file</span><span class="op">)</span>, <span class="op">]</span></span></code></pre></div>
 </div>
 <p>Before alignment, ensure the retention time (RT) ranges match between the datasets:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1]   9.674428 240.115311</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb19"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1]   0.275 480.176</code></pre>
@@ -280,7 +257,7 @@
 </div>
 <p>We need to adjust the RT range for the LC-MS/MS object to match the LC-MS data:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb21"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Filter the data to the same RT range as the LC-MS run</span></span>
 <span><span class="va">lcms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">filterRt</a></span><span class="op">(</span><span class="va">lcms2</span>, <span class="fu"><a href="https://rdrr.io/r/base/range.html" class="external-link">range</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
 </div>
@@ -288,27 +265,23 @@
 </h3>
 <p>To evaluate retention time shifts, we’ll plot the base peak chromatogram (BPC):</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">idx_A</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">sample_name</span> <span class="op">==</span> <span class="st">"A"</span><span class="op">)</span></span>
 <span><span class="va">idx_E</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">sample_name</span> <span class="op">==</span> <span class="st">"E"</span><span class="op">)</span></span>
 <span><span class="va">bpc1</span> <span class="op">&lt;-</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="va">idx_A</span>,<span class="va">idx_E</span><span class="op">)</span><span class="op">]</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span>,</span>
-<span>                    msLevel <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output cell-output-stderr">
-<pre><code>Processing chromatographic peaks</code></pre>
-</div>
-<div class="sourceCode cell-code" id="cb24"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="va">bpc2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms2</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span>, msLevel <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
+<span>                    msLevel <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span>
+<span><span class="va">bpc2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms2</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span>, msLevel <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
 </div>
 <p>Compare run1 sample A with run2 sample A</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb25"><pre class="downlit sourceCode r">
+<div class="cell" data-tbl-cap="Figure 1. BPC for sample A LC-MS and LC-MS/MS.">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bpc1</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span>, col <span class="op">=</span> <span class="st">"#00000080"</span>,</span>
 <span>     main <span class="op">=</span> <span class="st">"BPC sample A LC-MS vs A LC-MS/MS"</span>, lwd <span class="op">=</span> <span class="fl">1.5</span>, peakType <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/points.html" class="external-link">points</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime</a></span><span class="op">(</span><span class="va">bpc2</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity</a></span><span class="op">(</span><span class="va">bpc2</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span><span class="op">)</span>, col <span class="op">=</span> <span class="st">"#0000ff80"</span>, type <span class="op">=</span> <span class="st">"l"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topleft"</span>, col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"#00000080"</span>, <span class="st">"#0000ff80"</span><span class="op">)</span>,</span>
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"LC-MS"</span>, <span class="st">"LC-MS/MS"</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span>, lwd <span class="op">=</span> <span class="fl">2</span>, horiz <span class="op">=</span> <span class="cn">TRUE</span>, bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div>
 <figure><p><img src="alignment-to-external-dataset_files/figure-html/ini-bpc1-1.png" width="672"></p>
 </figure>
@@ -316,15 +289,15 @@
 </div>
 </div>
 <p>Similarly, compare the BPC for sample E:</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb26"><pre class="downlit sourceCode r">
+<div class="cell" data-tbl-cap="Figure 2. BPC for sample E LC-MS and LC-MS/MS.">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bpc1</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">]</span>, col <span class="op">=</span> <span class="st">"#00000080"</span>,</span>
 <span>     main <span class="op">=</span> <span class="st">"BPC sample E LC-MS vs E LC-MS/MS"</span>, lwd <span class="op">=</span> <span class="fl">1.5</span>, peakType <span class="op">=</span> <span class="st">"none"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/points.html" class="external-link">points</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime</a></span><span class="op">(</span><span class="va">bpc2</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity</a></span><span class="op">(</span><span class="va">bpc2</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span>, col <span class="op">=</span> <span class="st">"#0000ff80"</span>, type <span class="op">=</span> <span class="st">"l"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/legend.html" class="external-link">legend</a></span><span class="op">(</span><span class="st">"topleft"</span>, col <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"#00000080"</span>, <span class="st">"#0000ff80"</span><span class="op">)</span>,</span>
 <span>       legend <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"LC-MS"</span>, <span class="st">"LC-MS/MS"</span><span class="op">)</span>, lty <span class="op">=</span> <span class="fl">1</span>, lwd <span class="op">=</span> <span class="fl">2</span>, horiz <span class="op">=</span> <span class="cn">TRUE</span>, bty <span class="op">=</span> <span class="st">"n"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div>
 <figure><p><img src="alignment-to-external-dataset_files/figure-html/ini-bpc2-1.png" width="672"></p>
 </figure>
@@ -335,7 +308,7 @@
 </h3>
 <p>Perform peak detection and refining before alignment, as detailed in the end-to-end vignette. The same setting were applied.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb27"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">cwp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks-centWave.html" class="external-link">CentWaveParam</a></span><span class="op">(</span>peakwidth <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">8</span><span class="op">)</span>, ppm <span class="op">=</span> <span class="fl">15</span>, integrate <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
 <span><span class="va">lcms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/findChromPeaks.html" class="external-link">findChromPeaks</a></span><span class="op">(</span><span class="va">lcms2</span>, param <span class="op">=</span> <span class="va">cwp</span>, chunkSize <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></span>
 <span><span class="va">mnpp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/refineChromPeaks.html" class="external-link">MergeNeighboringPeaksParam</a></span><span class="op">(</span>expandRt <span class="op">=</span> <span class="fl">2.5</span>, expandMz <span class="op">=</span> <span class="fl">0.0015</span>,</span>
@@ -346,7 +319,7 @@
 </h3>
 <p>Now, we will attempt to align these two samples with the previous dataset. The first step is to extract landmark features (referred to as <em>lamas</em>). To achieve this, we will identify the features present in every phenotype group of the <code>lcms1</code> dataset. To do so, we will categorize (using <code><a href="https://rdrr.io/r/base/factor.html" class="external-link">factor()</a></code>) our data by <code>phenotype</code> and only retain the QC samples. This variable will be utilized to filter the features using the <code>PercentMissingFilter</code> parameter within the <code><a href="https://rdrr.io/pkg/ProtGenerics/man/filterFeatures.html" class="external-link">filterFeatures()</a></code> function. Here, by setting <code>threshold = 0</code> we select the features present in all QC samples.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb28"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">f</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/MsExperiment/man/MsExperiment.html" class="external-link">sampleData</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">$</span><span class="va">phenotype</span></span>
 <span><span class="va">f</span><span class="op">[</span><span class="va">f</span> <span class="op">!=</span> <span class="st">"QC"</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="cn">NA</span></span>
 <span><span class="va">lcms1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/filterFeatures.html" class="external-link">filterFeatures</a></span><span class="op">(</span><span class="va">lcms1</span>, <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/PercentMissingFilter.html" class="external-link">PercentMissingFilter</a></span><span class="op">(</span>threshold <span class="op">=</span> <span class="fl">0</span>, f <span class="op">=</span> <span class="va">f</span><span class="op">)</span>,</span>
@@ -354,7 +327,7 @@
 <div class="cell-output cell-output-stderr">
 <pre><code>3694 features were removed</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb30"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">lcms1_mz_rt</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/XCMSnExp-class.html" class="external-link">featureDefinitions</a></span><span class="op">(</span><span class="va">lcms1</span><span class="op">)</span><span class="op">[</span>, <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mzmed"</span>,<span class="st">"rtmed"</span><span class="op">)</span><span class="op">]</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">lcms1_mz_rt</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -366,7 +339,7 @@
 FT0005 53.52080 203.1936
 FT0007 54.01010 235.9032</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb32"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/pkg/BiocGenerics/man/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">lcms1_mz_rt</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1] 5374</code></pre>
@@ -375,20 +348,20 @@
 <p>This is what the <em>lamas</em> input should look like for alignment. In terms of how this method works, the alignment algorithm matches chromatographic peaks from the experimental data to the lamas, fitting a model based on this match to adjust their retention times and minimize differences between the two datasets.</p>
 <p>Now we can define our parameter object <code>LamaParama</code> to prepare for the alignment. Parameters such as <code>tolerance</code>, <code>toleranceRt</code>, and <code>ppm</code> relate to the matching between chromatographic peaks and lamas. Other parameters are related to the type of fitting generated between these data points. More details on each parameter and the overall method can be found by searching <code><a href="https://rdrr.io/pkg/xcms/man/adjustRtime.html" class="external-link">?adjustRtime</a></code>. Below is an example using default parameters.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb34"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">lp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/LamaParama.html" class="external-link">LamaParama</a></span><span class="op">(</span>lamas <span class="op">=</span> <span class="va">lcms1_mz_rt</span>, method <span class="op">=</span> <span class="st">"loess"</span>, span <span class="op">=</span> <span class="fl">0.5</span>,</span>
 <span>                 outlierTolerance <span class="op">=</span> <span class="fl">3</span>, zeroWeight <span class="op">=</span> <span class="fl">10</span>, ppm <span class="op">=</span><span class="fl">20</span>,</span>
 <span>                 tolerance <span class="op">=</span> <span class="fl">0</span>, toleranceRt <span class="op">=</span> <span class="fl">20</span>, bs <span class="op">=</span> <span class="st">"tp"</span><span class="op">)</span></span></code></pre></div>
 </div>
 <p>The <code>matchLamaChromPeaks()</code> function facilitates the assessment of how well the <em>lamas</em> correspond with the chromatographic peaks in each file. We then extract the matched results using the <code><a href="https://rdrr.io/pkg/xcms/man/LamaParama.html" class="external-link">matchedRtimes()</a></code> function. This will be used later to evaluate the alignment.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb35"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">lp</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/LamaParama.html" class="external-link">matchLamasChromPeaks</a></span><span class="op">(</span><span class="va">lcms2</span>, param <span class="op">=</span> <span class="va">lp</span><span class="op">)</span></span>
 <span><span class="va">ref_vs_obs</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/LamaParama.html" class="external-link">matchedRtimes</a></span><span class="op">(</span><span class="va">lp</span><span class="op">)</span></span></code></pre></div>
 </div>
 <p>Now we can adjust the retention time of the LC-MS/MS dataset using the <code><a href="https://rdrr.io/pkg/xcms/man/adjustRtime.html" class="external-link">adjustRtime()</a></code> function.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb36"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' input into `adjustRtime()`</span></span>
 <span><span class="va">lcms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/adjustRtime.html" class="external-link">adjustRtime</a></span><span class="op">(</span><span class="va">lcms2</span>, param <span class="op">=</span> <span class="va">lp</span><span class="op">)</span></span>
 <span><span class="va">lcms2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/applyAdjustedRtime.html" class="external-link">applyAdjustedRtime</a></span><span class="op">(</span><span class="va">lcms2</span><span class="op">)</span></span></code></pre></div>
@@ -397,16 +370,15 @@
 </h3>
 <p>We extract the base peak chromatogram (BPC) of our aligned object:</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb37"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co">#' evaluate the results with BPC</span></span>
-<span><span class="va">bpc2_adj</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms2</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span>,</span>
+<div class="sourceCode cell-code" id="cb25"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">bpc2_adj</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">chromatogram</a></span><span class="op">(</span><span class="va">lcms2</span>, aggregationFun <span class="op">=</span> <span class="st">"max"</span>,</span>
 <span>                         msLevel <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>
 </div>
 <section class="level3"><h4 id="visualizing-alignment-quality">Visualizing Alignment Quality<a class="anchor" aria-label="anchor" href="#visualizing-alignment-quality"></a>
 </h4>
 <p>To evaluate the performance of our alignment process, we generate plots comparing the alignment of the reference dataset (in black) with our LC-MS data before (in red) and after (in blue) the adjustment.</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb38"><pre class="downlit sourceCode r">
+<div class="cell" data-tbl-cap="Figure 3. BPC for sample A before and after alignment.">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' BPC of sample A</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>,  mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2.5</span>, <span class="fl">2.5</span>, <span class="fl">2.5</span>, <span class="fl">0.5</span><span class="op">)</span>, mgp <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.5</span>, <span class="fl">0.5</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bpc1</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span>, col <span class="op">=</span> <span class="st">"#00000080"</span>, main <span class="op">=</span> <span class="st">"Before Alignment"</span>, lwd <span class="op">=</span> <span class="fl">1.5</span>,</span>
@@ -424,15 +396,15 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/points.html" class="external-link">points</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime</a></span><span class="op">(</span><span class="va">bpc2_adj</span><span class="op">[</span><span class="fl">1</span>,<span class="fl">1</span><span class="op">]</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity</a></span><span class="op">(</span><span class="va">bpc2_adj</span><span class="op">[</span><span class="fl">1</span>,<span class="fl">1</span><span class="op">]</span><span class="op">)</span>,</span>
 <span>       type <span class="op">=</span> <span class="st">"l"</span>,</span>
 <span>       col <span class="op">=</span> <span class="st">"#0000ff80"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div>
-<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-15-1.png" width="576"></p>
+<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-16-1.png" width="576"></p>
 </figure>
 </div>
 </div>
 </div>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb39"><pre class="downlit sourceCode r">
+<div class="cell" data-tbl-cap="Figure 4. BPC for sample B before and after alignment.">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' BPC of sample B</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2</span>, <span class="fl">1</span><span class="op">)</span>,  mar <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">2.5</span>, <span class="fl">2.5</span>, <span class="fl">2.5</span>, <span class="fl">0.5</span><span class="op">)</span>, mgp <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1.5</span>, <span class="fl">0.5</span>, <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bpc1</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">]</span>, col <span class="op">=</span> <span class="st">"#00000080"</span>, main <span class="op">=</span> <span class="st">"Before Alignment"</span>, lwd <span class="op">=</span> <span class="fl">1.5</span>,</span>
@@ -448,17 +420,17 @@
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/points.html" class="external-link">points</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">rtime</a></span><span class="op">(</span><span class="va">bpc2_adj</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/pkg/ProtGenerics/man/protgenerics.html" class="external-link">intensity</a></span><span class="op">(</span><span class="va">bpc2_adj</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span>, type <span class="op">=</span> <span class="st">"l"</span>,</span>
 <span>       col <span class="op">=</span> <span class="st">"#0000ff80"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div>
-<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-16-1.png" width="576"></p>
+<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-17-1.png" width="576"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>Although the overall matching is imperfect due to initial sample issues, certain regions show significant improvement. The alignment of the signal’s start is particularly well done. Specifically, the regions right before and after 150 seconds show substantial improvement.</p>
 <p>Below is a visualization of the distribution of chromatographic peaks matched to anchor peaks (lamas) for Sample A. The red vertical lines represent the positions of these matched peaks.</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb40"><pre class="downlit sourceCode r">
+<div class="cell" data-tbl-cap="Figure 5. Distribution of CP matched to Lamas for sample A.">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb28"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' BPC of the first sample with matches to lamas overlay</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/par.html" class="external-link">par</a></span><span class="op">(</span>mfrow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">bpc1</span><span class="op">[</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">]</span>, col <span class="op">=</span> <span class="st">"#00000080"</span>, main <span class="op">=</span> <span class="st">"Distribution CP matched to Lamas"</span>,</span>
@@ -468,9 +440,9 @@
 <span>       col <span class="op">=</span> <span class="st">"#0000ff80"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span>v <span class="op">=</span> <span class="va">ref_vs_obs</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">obs</span>, col <span class="op">=</span> <span class="st">"#c4114510"</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div>
-<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-17-1.png" width="672"></p>
+<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-18-1.png" width="672"></p>
 </figure>
 </div>
 </div>
@@ -478,8 +450,8 @@
 </section><section class="level3"><h4 id="quantitative-evaluation-of-alignment">Quantitative Evaluation of Alignment<a class="anchor" aria-label="anchor" href="#quantitative-evaluation-of-alignment"></a>
 </h4>
 <p>To quantitatively assess the quality of the alignment, we compute the distance between the chromatographic peaks in our LC-MS data and the anchor peaks (Lamas) both before and after the alignment.</p>
-<div class="cell">
-<div class="sourceCode cell-code" id="cb41"><pre class="downlit sourceCode r">
+<div class="cell" data-tbl-cap="Figure 6. Distance to Anchor Peaks: Before vs. After Alignment.">
+<div class="sourceCode cell-code" id="cb29"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Extract data for sample 3 directly</span></span>
 <span><span class="va">ref_obs_sample_1</span> <span class="op">&lt;-</span> <span class="va">ref_vs_obs</span><span class="op">[[</span><span class="st">"1"</span><span class="op">]</span><span class="op">]</span></span>
 <span></span>
@@ -507,14 +479,14 @@
 <span>        main <span class="op">=</span> <span class="st">"Distance to Anchor Peaks: Before vs. After Alignment"</span><span class="op">)</span></span></code></pre></div>
 <div class="cell-output-display">
 <div>
-<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-18-1.png" width="672"></p>
+<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-19-1.png" width="672"></p>
 </figure>
 </div>
 </div>
 </div>
 <p>Furthermore, a more detailed examination of the matching and the model used for fitting each file is possible. Numerical information can be obtained using the <code><a href="https://rdrr.io/pkg/xcms/man/LamaParama.html" class="external-link">summarizeLamaMatch()</a></code> function. From this, the percentage of chromatographic peaks utilized for alignment can be computed relative to the total number of peaks in the file. Additionally, it is feasible to directly <code><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot()</a></code> the <code>param</code> object for the file of interest, showcasing the distribution of these chromatographic peaks along with the fitted model line.</p>
 <div class="cell">
-<div class="sourceCode cell-code" id="cb42"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb30"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Access summary of matches and model information</span></span>
 <span><span class="va">summary</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/xcms/man/LamaParama.html" class="external-link">summarizeLamaMatch</a></span><span class="op">(</span><span class="va">lp</span><span class="op">)</span></span>
 <span><span class="va">summary</span></span></code></pre></div>
@@ -524,13 +496,13 @@
 2        6860          1785        5374  1617, c(....
 3        7588          2082        5374  1869, c(....</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb44"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb32"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Coverage for each file</span></span>
 <span><span class="va">summary</span><span class="op">$</span><span class="va">Matched_peaks</span> <span class="op">/</span> <span class="va">summary</span><span class="op">$</span><span class="va">Total_peaks</span> <span class="op">*</span> <span class="fl">100</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1] 26.71253 26.02041 27.43806</code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb46"><pre class="downlit sourceCode r">
+<div class="sourceCode cell-code" id="cb34"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Access the information on the model of for the first file</span></span>
 <span><span class="va">summary</span><span class="op">$</span><span class="va">Model_summary</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -552,15 +524,22 @@
  parametric:  FALSE
 drop.square:  FALSE </code></pre>
 </div>
-<div class="sourceCode cell-code" id="cb48"><pre class="downlit sourceCode r">
+</div>
+<div class="cell" data-tbl-cap="Figure 7. Model fit for the first file.">
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb36"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co">#' Plot obs vs. lcms1 with fitting line</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">lp</span>, index <span class="op">=</span> <span class="fl">1L</span>, main <span class="op">=</span> <span class="st">"ChromPeaks versus Lamas for sample A"</span>,</span>
-<span>     colPoint <span class="op">=</span> <span class="st">"red"</span><span class="op">)</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, lty <span class="op">=</span> <span class="fl">3</span>, col <span class="op">=</span> <span class="st">"grey"</span><span class="op">)</span></span>
+<span>     colPoint <span class="op">=</span> <span class="st">"red"</span><span class="op">)</span></span></code></pre></div>
+</details><div class="cell-output cell-output-stderr">
+<pre><code>Warning in rbind(deparse.level, ...): number of columns of result, 3, is not a
+multiple of vector length 2 of arg 1</code></pre>
+</div>
+<details class="code-fold"><summary>Show the code</summary><div class="sourceCode cell-code" id="cb38"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/graphics/abline.html" class="external-link">abline</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, lty <span class="op">=</span> <span class="fl">3</span>, col <span class="op">=</span> <span class="st">"grey"</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/graphics/grid.html" class="external-link">grid</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<div class="cell-output-display">
+</details><div class="cell-output-display">
 <div>
-<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-19-1.png" width="672"></p>
+<figure><p><img src="alignment-to-external-dataset_files/figure-html/unnamed-chunk-21-1.png" width="672"></p>
 </figure>
 </div>
 </div>
diff --git a/articles/alignment-to-external-dataset_files/figure-html/ini-bpc1-1.png b/articles/alignment-to-external-dataset_files/figure-html/ini-bpc1-1.png
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diff --git a/articles/index.html b/articles/index.html
index d7d6a2a..655051e 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -44,8 +44,6 @@ <h3>All vignettes</h3>
         <dd>
         </dd><dt><a href="alignment-to-external-dataset.html">Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset</a></dt>
         <dd>
-        </dd><dt><a href="dataset-investigation.html">Dataset investigation: What to do when you get your data</a></dt>
-        <dd>
         </dd><dt><a href="install_v0.html">Install</a></dt>
         <dd>
       </dd></dl></div>
diff --git a/articles/install_v0.html b/articles/install_v0.html
index 5933d46..3772b30 100644
--- a/articles/install_v0.html
+++ b/articles/install_v0.html
@@ -14,8 +14,8 @@
 <link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png">
 <script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><script src="install_v0_files/libs/quarto-html/popper.min.js"></script><script src="install_v0_files/libs/quarto-html/tippy.umd.min.js"></script><link href="install_v0_files/libs/quarto-html/tippy.css" rel="stylesheet">
 <link href="install_v0_files/libs/quarto-html/light-border.css" rel="stylesheet">
 <!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Install">
@@ -29,7 +29,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -62,10 +62,10 @@
 </nav><div class="container template-quarto">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="../logo.png" class="logo" alt=""><h1>Install</h1>
+      <img src="../logo.png" class="logo" alt=""><h1></h1>
 
 
-      <p class="date">2024-10-30</p>
+      <p class="date">2024-12-04</p>
 
       <small class="dont-index">Source: <a href="https://github.com/rformassspectrometry/metabonaut/blob/main/vignettes/install_v0.qmd" class="external-link"><code>vignettes/install_v0.qmd</code></a></small>
       <div class="d-none name"><code></code></div>
@@ -76,7 +76,7 @@
 
 
 
-    <p>For manual installation, an <a href="https://www.r-project.org/" class="external-link">R</a> version &gt;= 4.4.0 is required.</p>
+    <header id="title-block-header" class="quarto-title-block"><h1 class="title">Install</h1></header><p>For manual installation, an <a href="https://www.r-project.org/" class="external-link">R</a> version &gt;= 4.4.0 is required.</p>
 <p><u>Notes</u>: only a development version of <code>metabonaut</code> is available as of now but a stable version will come soon. Therefore, if you are having difficulty to run some part of the code we strongly recommend to use the Docker image, as it will have all the package necessary to run all the analysis presented in these workflows.</p>
 <section class="section level2"><h2 id="running-workflows-locally">Running workflows locally<a class="anchor" aria-label="anchor" href="#running-workflows-locally"></a>
 </h2>
diff --git a/authors.html b/authors.html
index b3c9351..92b0ef9 100644
--- a/authors.html
+++ b/authors.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/deps/data-deps.txt b/deps/data-deps.txt
index 8aa49e2..ca4dfd9 100644
--- a/deps/data-deps.txt
+++ b/deps/data-deps.txt
@@ -2,8 +2,8 @@
 <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
 <script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
-<link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet" />
-<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet" />
 <script src="deps/headroom-0.11.0/headroom.min.js"></script>
 <script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
 <script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
diff --git a/deps/font-awesome-6.5.2/css/all.css b/deps/font-awesome-6.5.2/css/all.css
new file mode 100644
index 0000000..151dd57
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/all.css
@@ -0,0 +1,8028 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, 0));
+          transform: rotate(var(--fa-rotate-angle, 0)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
+.fa-paste::before {
+  content: "\f0ea"; }
+
+.fa-file-clipboard::before {
+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
+
+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
+.fa-blog::before {
+  content: "\f781"; }
+
+.fa-user-ninja::before {
+  content: "\f504"; }
+
+.fa-person-arrow-up-from-line::before {
+  content: "\e539"; }
+
+.fa-scroll-torah::before {
+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
+
+.fa-broom-ball::before {
+  content: "\f458"; }
+
+.fa-quidditch::before {
+  content: "\f458"; }
+
+.fa-quidditch-broom-ball::before {
+  content: "\f458"; }
+
+.fa-toggle-off::before {
+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
+.fa-arrow-down-9-1::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-desc::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-down-alt::before {
+  content: "\f886"; }
+
+.fa-face-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
+
+.fa-trowel-bricks::before {
+  content: "\e58a"; }
+
+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
+
+.fa-hospital-user::before {
+  content: "\f80d"; }
+
+.fa-tent-arrow-left-right::before {
+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
+.fa-square-caret-down::before {
+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
+
+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
+.fa-chevron-circle-down::before {
+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-table-cells-column-lock::before {
+  content: "\e678"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
+.fa-rectangle-ad::before {
+  content: "\f641"; }
+
+.fa-ad::before {
+  content: "\f641"; }
+
+.fa-circle-arrow-right::before {
+  content: "\f0a9"; }
+
+.fa-arrow-circle-right::before {
+  content: "\f0a9"; }
+
+.fa-filter-circle-xmark::before {
+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
+
+.fa-list-ol::before {
+  content: "\f0cb"; }
+
+.fa-list-1-2::before {
+  content: "\f0cb"; }
+
+.fa-list-numeric::before {
+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
+.fa-question::before {
+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
+.fa-up-down-left-right::before {
+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
+.fa-cubes-stacked::before {
+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
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+  content: "\f177"; }
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+  content: "\f177"; }
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+  content: "\e55c"; }
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+  content: "\f21d"; }
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+  content: "\e18f"; }
+
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+  content: "\f026"; }
+
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+  content: "\f2a3"; }
+
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+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
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+  content: "\f2a3"; }
+
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+  content: "\f013"; }
+
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+  content: "\f013"; }
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+  content: "\f5c7"; }
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+  content: "\f5c7"; }
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+  content: "\f678"; }
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+.fa-mosquito::before {
+  content: "\e52b"; }
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+  content: "\f69a"; }
+
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+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
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+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
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+  content: "\e55f"; }
+
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+  content: "\f67f"; }
+
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+  content: "\f58e"; }
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+  content: "\f148"; }
+
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+  content: "\f148"; }
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+  content: "\55"; }
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+  content: "\f698"; }
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+  content: "\f698"; }
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+  content: "\f017"; }
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+  content: "\f017"; }
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+  content: "\f048"; }
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+.fa-step-backward::before {
+  content: "\f048"; }
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+  content: "\f482"; }
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+  content: "\e005"; }
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+  content: "\f432"; }
+
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+  content: "\53"; }
+
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+  content: "\e29c"; }
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+.fa-keyboard::before {
+  content: "\f11c"; }
+
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+  content: "\f0d7"; }
+
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+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
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+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
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+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
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+.fa-khanda::before {
+  content: "\f66d"; }
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+  content: "\f1de"; }
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+  content: "\f1de"; }
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+  content: "\f802"; }
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+.fa-network-wired::before {
+  content: "\f6ff"; }
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+  content: "\f276"; }
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+.fa-hamsa::before {
+  content: "\f665"; }
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+  content: "\e3f5"; }
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+.fa-flask::before {
+  content: "\f0c3"; }
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+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
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+  content: "\f72b"; }
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+  content: "\f142"; }
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+  content: "\f142"; }
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+.fa-ticket::before {
+  content: "\f145"; }
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+.fa-power-off::before {
+  content: "\f011"; }
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+.fa-right-long::before {
+  content: "\f30b"; }
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+  content: "\f30b"; }
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+.fa-flag-usa::before {
+  content: "\f74d"; }
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+.fa-laptop-file::before {
+  content: "\e51d"; }
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+.fa-tty::before {
+  content: "\f1e4"; }
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+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
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+.fa-person-rifle::before {
+  content: "\e54e"; }
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+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
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+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
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+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
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+.fa-n::before {
+  content: "\4e"; }
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+.fa-cable-car::before {
+  content: "\f7da"; }
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+.fa-tram::before {
+  content: "\f7da"; }
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+.fa-cloud-rain::before {
+  content: "\f73d"; }
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+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
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+  content: "\e4b8"; }
+
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+  content: "\f019"; }
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+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
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+  content: "\f55a"; }
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+  content: "\f55a"; }
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+.fa-eye-dropper::before {
+  content: "\f1fb"; }
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+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
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+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
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+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
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+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
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+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
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+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
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+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
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+.fa-file-medical::before {
+  content: "\f477"; }
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+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
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+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
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+  content: "\f161"; }
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+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
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+  content: "\f30c"; }
+
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+  content: "\f04d"; }
+
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+  content: "\f387"; }
+
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+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
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+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
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+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
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+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
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+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
+.fa-first-aid::before {
+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
+
+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
+
+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
+
+.fa-chain::before {
+  content: "\f0c1"; }
+
+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-table-cells-row-lock::before {
+  content: "\e67a"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
+  content: "\f1e9"; }
+
+.fa-cc-visa:before {
+  content: "\f1f0"; }
+
+.fa-lastfm:before {
+  content: "\f202"; }
+
+.fa-shopware:before {
+  content: "\f5b5"; }
+
+.fa-creative-commons-nc:before {
+  content: "\f4e8"; }
+
+.fa-aws:before {
+  content: "\f375"; }
+
+.fa-redhat:before {
+  content: "\f7bc"; }
+
+.fa-yoast:before {
+  content: "\f2b1"; }
+
+.fa-cloudflare:before {
+  content: "\e07d"; }
+
+.fa-ups:before {
+  content: "\f7e0"; }
+
+.fa-pixiv:before {
+  content: "\e640"; }
+
+.fa-wpexplorer:before {
+  content: "\f2de"; }
+
+.fa-dyalog:before {
+  content: "\f399"; }
+
+.fa-bity:before {
+  content: "\f37a"; }
+
+.fa-stackpath:before {
+  content: "\f842"; }
+
+.fa-buysellads:before {
+  content: "\f20d"; }
+
+.fa-first-order:before {
+  content: "\f2b0"; }
+
+.fa-modx:before {
+  content: "\f285"; }
+
+.fa-guilded:before {
+  content: "\e07e"; }
+
+.fa-vnv:before {
+  content: "\f40b"; }
+
+.fa-square-js:before {
+  content: "\f3b9"; }
+
+.fa-js-square:before {
+  content: "\f3b9"; }
+
+.fa-microsoft:before {
+  content: "\f3ca"; }
+
+.fa-qq:before {
+  content: "\f1d6"; }
+
+.fa-orcid:before {
+  content: "\f8d2"; }
+
+.fa-java:before {
+  content: "\f4e4"; }
+
+.fa-invision:before {
+  content: "\f7b0"; }
+
+.fa-creative-commons-pd-alt:before {
+  content: "\f4ed"; }
+
+.fa-centercode:before {
+  content: "\f380"; }
+
+.fa-glide-g:before {
+  content: "\f2a6"; }
+
+.fa-drupal:before {
+  content: "\f1a9"; }
+
+.fa-jxl:before {
+  content: "\e67b"; }
+
+.fa-hire-a-helper:before {
+  content: "\f3b0"; }
+
+.fa-creative-commons-by:before {
+  content: "\f4e7"; }
+
+.fa-unity:before {
+  content: "\e049"; }
+
+.fa-whmcs:before {
+  content: "\f40d"; }
+
+.fa-rocketchat:before {
+  content: "\f3e8"; }
+
+.fa-vk:before {
+  content: "\f189"; }
+
+.fa-untappd:before {
+  content: "\f405"; }
+
+.fa-mailchimp:before {
+  content: "\f59e"; }
+
+.fa-css3-alt:before {
+  content: "\f38b"; }
+
+.fa-square-reddit:before {
+  content: "\f1a2"; }
+
+.fa-reddit-square:before {
+  content: "\f1a2"; }
+
+.fa-vimeo-v:before {
+  content: "\f27d"; }
+
+.fa-contao:before {
+  content: "\f26d"; }
+
+.fa-square-font-awesome:before {
+  content: "\e5ad"; }
+
+.fa-deskpro:before {
+  content: "\f38f"; }
+
+.fa-brave:before {
+  content: "\e63c"; }
+
+.fa-sistrix:before {
+  content: "\f3ee"; }
+
+.fa-square-instagram:before {
+  content: "\e055"; }
+
+.fa-instagram-square:before {
+  content: "\e055"; }
+
+.fa-battle-net:before {
+  content: "\f835"; }
+
+.fa-the-red-yeti:before {
+  content: "\f69d"; }
+
+.fa-square-hacker-news:before {
+  content: "\f3af"; }
+
+.fa-hacker-news-square:before {
+  content: "\f3af"; }
+
+.fa-edge:before {
+  content: "\f282"; }
+
+.fa-threads:before {
+  content: "\e618"; }
+
+.fa-napster:before {
+  content: "\f3d2"; }
+
+.fa-square-snapchat:before {
+  content: "\f2ad"; }
+
+.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa-google-plus-g:before {
+  content: "\f0d5"; }
+
+.fa-artstation:before {
+  content: "\f77a"; }
+
+.fa-markdown:before {
+  content: "\f60f"; }
+
+.fa-sourcetree:before {
+  content: "\f7d3"; }
+
+.fa-google-plus:before {
+  content: "\f2b3"; }
+
+.fa-diaspora:before {
+  content: "\f791"; }
+
+.fa-foursquare:before {
+  content: "\f180"; }
+
+.fa-stack-overflow:before {
+  content: "\f16c"; }
+
+.fa-github-alt:before {
+  content: "\f113"; }
+
+.fa-phoenix-squadron:before {
+  content: "\f511"; }
+
+.fa-pagelines:before {
+  content: "\f18c"; }
+
+.fa-algolia:before {
+  content: "\f36c"; }
+
+.fa-red-river:before {
+  content: "\f3e3"; }
+
+.fa-creative-commons-sa:before {
+  content: "\f4ef"; }
+
+.fa-safari:before {
+  content: "\f267"; }
+
+.fa-google:before {
+  content: "\f1a0"; }
+
+.fa-square-font-awesome-stroke:before {
+  content: "\f35c"; }
+
+.fa-font-awesome-alt:before {
+  content: "\f35c"; }
+
+.fa-atlassian:before {
+  content: "\f77b"; }
+
+.fa-linkedin-in:before {
+  content: "\f0e1"; }
+
+.fa-digital-ocean:before {
+  content: "\f391"; }
+
+.fa-nimblr:before {
+  content: "\f5a8"; }
+
+.fa-chromecast:before {
+  content: "\f838"; }
+
+.fa-evernote:before {
+  content: "\f839"; }
+
+.fa-hacker-news:before {
+  content: "\f1d4"; }
+
+.fa-creative-commons-sampling:before {
+  content: "\f4f0"; }
+
+.fa-adversal:before {
+  content: "\f36a"; }
+
+.fa-creative-commons:before {
+  content: "\f25e"; }
+
+.fa-watchman-monitoring:before {
+  content: "\e087"; }
+
+.fa-fonticons:before {
+  content: "\f280"; }
+
+.fa-weixin:before {
+  content: "\f1d7"; }
+
+.fa-shirtsinbulk:before {
+  content: "\f214"; }
+
+.fa-codepen:before {
+  content: "\f1cb"; }
+
+.fa-git-alt:before {
+  content: "\f841"; }
+
+.fa-lyft:before {
+  content: "\f3c3"; }
+
+.fa-rev:before {
+  content: "\f5b2"; }
+
+.fa-windows:before {
+  content: "\f17a"; }
+
+.fa-wizards-of-the-coast:before {
+  content: "\f730"; }
+
+.fa-square-viadeo:before {
+  content: "\f2aa"; }
+
+.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa-meetup:before {
+  content: "\f2e0"; }
+
+.fa-centos:before {
+  content: "\f789"; }
+
+.fa-adn:before {
+  content: "\f170"; }
+
+.fa-cloudsmith:before {
+  content: "\f384"; }
+
+.fa-opensuse:before {
+  content: "\e62b"; }
+
+.fa-pied-piper-alt:before {
+  content: "\f1a8"; }
+
+.fa-square-dribbble:before {
+  content: "\f397"; }
+
+.fa-dribbble-square:before {
+  content: "\f397"; }
+
+.fa-codiepie:before {
+  content: "\f284"; }
+
+.fa-node:before {
+  content: "\f419"; }
+
+.fa-mix:before {
+  content: "\f3cb"; }
+
+.fa-steam:before {
+  content: "\f1b6"; }
+
+.fa-cc-apple-pay:before {
+  content: "\f416"; }
+
+.fa-scribd:before {
+  content: "\f28a"; }
+
+.fa-debian:before {
+  content: "\e60b"; }
+
+.fa-openid:before {
+  content: "\f19b"; }
+
+.fa-instalod:before {
+  content: "\e081"; }
+
+.fa-expeditedssl:before {
+  content: "\f23e"; }
+
+.fa-sellcast:before {
+  content: "\f2da"; }
+
+.fa-square-twitter:before {
+  content: "\f081"; }
+
+.fa-twitter-square:before {
+  content: "\f081"; }
+
+.fa-r-project:before {
+  content: "\f4f7"; }
+
+.fa-delicious:before {
+  content: "\f1a5"; }
+
+.fa-freebsd:before {
+  content: "\f3a4"; }
+
+.fa-vuejs:before {
+  content: "\f41f"; }
+
+.fa-accusoft:before {
+  content: "\f369"; }
+
+.fa-ioxhost:before {
+  content: "\f208"; }
+
+.fa-fonticons-fi:before {
+  content: "\f3a2"; }
+
+.fa-app-store:before {
+  content: "\f36f"; }
+
+.fa-cc-mastercard:before {
+  content: "\f1f1"; }
+
+.fa-itunes-note:before {
+  content: "\f3b5"; }
+
+.fa-golang:before {
+  content: "\e40f"; }
+
+.fa-kickstarter:before {
+  content: "\f3bb"; }
+
+.fa-square-kickstarter:before {
+  content: "\f3bb"; }
+
+.fa-grav:before {
+  content: "\f2d6"; }
+
+.fa-weibo:before {
+  content: "\f18a"; }
+
+.fa-uncharted:before {
+  content: "\e084"; }
+
+.fa-firstdraft:before {
+  content: "\f3a1"; }
+
+.fa-square-youtube:before {
+  content: "\f431"; }
+
+.fa-youtube-square:before {
+  content: "\f431"; }
+
+.fa-wikipedia-w:before {
+  content: "\f266"; }
+
+.fa-wpressr:before {
+  content: "\f3e4"; }
+
+.fa-rendact:before {
+  content: "\f3e4"; }
+
+.fa-angellist:before {
+  content: "\f209"; }
+
+.fa-galactic-republic:before {
+  content: "\f50c"; }
+
+.fa-nfc-directional:before {
+  content: "\e530"; }
+
+.fa-skype:before {
+  content: "\f17e"; }
+
+.fa-joget:before {
+  content: "\f3b7"; }
+
+.fa-fedora:before {
+  content: "\f798"; }
+
+.fa-stripe-s:before {
+  content: "\f42a"; }
+
+.fa-meta:before {
+  content: "\e49b"; }
+
+.fa-laravel:before {
+  content: "\f3bd"; }
+
+.fa-hotjar:before {
+  content: "\f3b1"; }
+
+.fa-bluetooth-b:before {
+  content: "\f294"; }
+
+.fa-square-letterboxd:before {
+  content: "\e62e"; }
+
+.fa-sticker-mule:before {
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+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/deps/font-awesome-6.5.2/css/all.min.css b/deps/font-awesome-6.5.2/css/all.min.css
new file mode 100644
index 0000000..269bcee
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,steps(8));animation-timing-function:var(--fa-animation-timing,steps(8))}@media (prefers-reduced-motion:reduce){.fa-beat,.fa-beat-fade,.fa-bounce,.fa-fade,.fa-flip,.fa-pulse,.fa-shake,.fa-spin,.fa-spin-pulse{-webkit-animation-delay:-1ms;animation-delay:-1ms;-webkit-animation-duration:1ms;animation-duration:1ms;-webkit-animation-iteration-count:1;animation-iteration-count:1;-webkit-transition-delay:0s;transition-delay:0s;-webkit-transition-duration:0s;transition-duration:0s}}@-webkit-keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@-webkit-keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@-webkit-keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@-webkit-keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@-webkit-keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@-webkit-keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes 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+
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diff --git a/deps/font-awesome-6.5.2/css/v4-shims.css b/deps/font-awesome-6.5.2/css/v4-shims.css
new file mode 100644
index 0000000..ea60ea4
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
+
+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-o:before {
+  content: "\f15b"; }
+
+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
+
+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
+
+.fa.fa-video-camera:before {
+  content: "\f03d"; }
+
+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-picture-o:before {
+  content: "\f03e"; }
+
+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-photo:before {
+  content: "\f03e"; }
+
+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-image:before {
+  content: "\f03e"; }
+
+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
+
+.fa.fa-pencil-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
+
+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-edit:before {
+  content: "\f044"; }
+
+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
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+.fa.fa-check-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
+
+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
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+.fa.fa-check-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
+  content: "\f058"; }
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+.fa.fa-mail-forward:before {
+  content: "\f064"; }
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+.fa.fa-expand:before {
+  content: "\f424"; }
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+.fa.fa-modx {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fort-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-usb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-product-hunt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-mixcloud {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-scribd {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/deps/font-awesome-6.5.2/css/v4-shims.min.css b/deps/font-awesome-6.5.2/css/v4-shims.min.css
new file mode 100644
index 0000000..09baf5f
--- /dev/null
+++ b/deps/font-awesome-6.5.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 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Awesome 6 Free";font-weight:400}.fa.fa-check-square-o:before{content:"\f14a"}.fa.fa-arrows:before{content:"\f0b2"}.fa.fa-times-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-circle-o:before{content:"\f057"}.fa.fa-check-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-circle-o:before{content:"\f058"}.fa.fa-mail-forward:before{content:"\f064"}.fa.fa-expand:before{content:"\f424"}.fa.fa-compress:before{content:"\f422"}.fa.fa-eye,.fa.fa-eye-slash{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-warning:before{content:"\f071"}.fa.fa-calendar:before{content:"\f073"}.fa.fa-arrows-v:before{content:"\f338"}.fa.fa-arrows-h:before{content:"\f337"}.fa.fa-bar-chart-o:before,.fa.fa-bar-chart:before{content:"\e0e3"}.fa.fa-twitter-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-twitter-square:before{content:"\f081"}.fa.fa-facebook-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-square:before{content:"\f082"}.fa.fa-gears:before{content:"\f085"}.fa.fa-thumbs-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-up:before{content:"\f164"}.fa.fa-thumbs-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-down:before{content:"\f165"}.fa.fa-heart-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-heart-o:before{content:"\f004"}.fa.fa-sign-out:before{content:"\f2f5"}.fa.fa-linkedin-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin-square:before{content:"\f08c"}.fa.fa-thumb-tack:before{content:"\f08d"}.fa.fa-external-link:before{content:"\f35d"}.fa.fa-sign-in:before{content:"\f2f6"}.fa.fa-github-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-github-square:before{content:"\f092"}.fa.fa-lemon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lemon-o:before{content:"\f094"}.fa.fa-square-o{font-family:"Font Awesome 6 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new file mode 100644
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diff --git a/index.html b/index.html
index ccf5668..57514f5 100644
--- a/index.html
+++ b/index.html
@@ -14,8 +14,8 @@
 <link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png">
 <script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
 <link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
-<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet">
-<link href="deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
 <script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Exploring and Analyzing LC-MS Data">
 <meta name="description" content="This resource hosts tutorials and end-to-end workflows describing how to analyze LC-MS/MS data, from raw files to annotation, using Bioconductor packages.">
 <meta property="og:description" content="This resource hosts tutorials and end-to-end workflows describing how to analyze LC-MS/MS data, from raw files to annotation, using Bioconductor packages.">
@@ -29,7 +29,7 @@
 
     <a class="navbar-brand me-2" href="index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index a14c428..21909c5 100644
--- a/news/index.html
+++ b/news/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Changelog • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Changelog"><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -40,7 +40,13 @@
     <div class="section level2">
 <h2 class="pkg-version" data-toc-text="0.0" id="metabonaut-00">metabonaut 0.0<a class="anchor" aria-label="anchor" href="#metabonaut-00"></a></h2>
 <div class="section level3">
-<h3 id="changes-in-0-0">Changes in 0.0.5<a class="anchor" aria-label="anchor" href="#changes-in-0-0"></a></h3>
+<h3 id="changes-in-0-0">Changes in 0.0.6<a class="anchor" aria-label="anchor" href="#changes-in-0-0"></a></h3>
+<ul><li>Moving The PercentMissing filtering at the end of the pre-processing steps as it needs to be done before normalization.</li>
+<li>Addition of collapsing code to improve readability.</li>
+<li>Reduction of table size</li>
+</ul></div>
+<div class="section level3">
+<h3 id="changes-in-0-0-1">Changes in 0.0.5<a class="anchor" aria-label="anchor" href="#changes-in-0-0-1"></a></h3>
 <ul><li>Require <em>MsIO</em> version 0.0.8 to allow reading of stored <code>MsBackendMetaboLights</code> objects.</li>
 <li>Small updates and changes to the Seamless Alignment vignette:
 <ul><li>simplify import of previously stored result object</li>
@@ -48,7 +54,7 @@ <h3 id="changes-in-0-0">Changes in 0.0.5<a class="anchor" aria-label="anchor" hr
 </ul></li>
 </ul></div>
 <div class="section level3">
-<h3 id="changes-in-0-0-1">Changes in 0.0.4<a class="anchor" aria-label="anchor" href="#changes-in-0-0-1"></a></h3>
+<h3 id="changes-in-0-0-2">Changes in 0.0.4<a class="anchor" aria-label="anchor" href="#changes-in-0-0-2"></a></h3>
 <ul><li>Required <em>alabaster.se</em>.</li>
 <li>In the end-to-end vignette:
 <ul><li>Removal of Spectra data in depth visualisation to move to the Data investigation vignette</li>
@@ -58,18 +64,18 @@ <h3 id="changes-in-0-0-1">Changes in 0.0.4<a class="anchor" aria-label="anchor"
 </li>
 </ul></div>
 <div class="section level3">
-<h3 id="changes-in-0-0-2">Changes in 0.0.3<a class="anchor" aria-label="anchor" href="#changes-in-0-0-2"></a></h3>
+<h3 id="changes-in-0-0-3">Changes in 0.0.3<a class="anchor" aria-label="anchor" href="#changes-in-0-0-3"></a></h3>
 <ul><li>Require <em>xcms</em> version 4.3.4 and install the package from github.</li>
 </ul></div>
 <div class="section level3">
-<h3 id="changes-in-0-0-3">Changes in 0.0.2<a class="anchor" aria-label="anchor" href="#changes-in-0-0-3"></a></h3>
+<h3 id="changes-in-0-0-4">Changes in 0.0.2<a class="anchor" aria-label="anchor" href="#changes-in-0-0-4"></a></h3>
 <ul><li>Switch to Quarto instead of Rmarkdown</li>
 <li>Addition of Alignment to reference dataset vignette</li>
 <li>Addition of the Data investigation vignette</li>
 <li>Addition of the Install vignette</li>
 </ul></div>
 <div class="section level3">
-<h3 id="changes-in-0-0-4">Changes in 0.0.1<a class="anchor" aria-label="anchor" href="#changes-in-0-0-4"></a></h3>
+<h3 id="changes-in-0-0-5">Changes in 0.0.1<a class="anchor" aria-label="anchor" href="#changes-in-0-0-5"></a></h3>
 <ul><li>Addition of basic files for a workflow package.</li>
 <li>Addition of the end-to-end vignette.</li>
 </ul></div>
diff --git a/pkgdown.yml b/pkgdown.yml
index c9e2a6c..0707f9b 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -1,12 +1,11 @@
-pandoc: '3.4'
+pandoc: '3.5'
 pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
   a-end-to-end-untargeted-metabolomics: a-end-to-end-untargeted-metabolomics.html
   alignment-to-external-dataset: alignment-to-external-dataset.html
-  dataset-investigation: dataset-investigation.html
   install_v0: install_v0.html
-last_built: 2024-10-30T16:07Z
+last_built: 2024-12-04T10:37Z
 urls:
   reference: https://rformassspectrometry.github.io/metabonaut/reference
   article: https://rformassspectrometry.github.io/metabonaut/articles
diff --git a/reference/index.html b/reference/index.html
index 6da76f4..cfa80f7 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -1,5 +1,5 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title> • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.4.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content=""><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title> • metabonaut</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content=""><meta property="og:image" content="https://rformassspectrometry.github.io/metabonaut/logo.png"></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
 
 
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">metabonaut</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.4</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.0.6</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index 90bd921..4e35da1 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"present workflow describes steps analysis LC-MS/MS experiment, includes preprocessing raw data generate abundance matrix features various samples, followed data normalization, differential abundance analysis finally annotation features metabolites. Note also alternative analysis options R packages used different steps examples mentioned throughout workflow.  Steps end--end workflow possible alternatives","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-description","dir":"Articles","previous_headings":"","what":"Data description","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"See data description vignette detailed explanation dataset go workflow general tips done first get data.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"packages-needed","dir":"Articles","previous_headings":"","what":"Packages needed","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"workflow therefore based following dependencies:","code":"## Data Import and handling library(readxl) library(MsExperiment) library(MsIO) library(alabaster.se) library(MsBackendMetaboLights) library(SummarizedExperiment)  ## Preprocessing of LC-MS data library(xcms) library(Spectra) library(MetaboCoreUtils)  ## Statistical analysis library(limma) # Differential abundance library(matrixStats) # Summaries over matrices  ## Visualisation library(pander) library(RColorBrewer) library(pheatmap) library(vioplot) library(ggfortify)   # Plot PCA library(gridExtra)   # To arrange multiple ggplots into single plots  ## Annotation library(AnnotationHub) # Annotation resources library(CompoundDb)    # Access small compound annotation data. library(MetaboAnnotation) # Functionality for metabolite annotation."},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-import","dir":"Articles","previous_headings":"","what":"Data import","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Note different equipment generate various file extensions, conversion step might needed beforehand, though apply dataset. Spectra package supports variety ways store retrieve MS data, including mzML, mzXML, CDF files, simple flat files, database systems. necessary, several tools, ProteoWizard’s MSConvert, can used convert files .mzML format (Chambers et al. 2012). show extract dataset MetaboLigths database load MsExperiment object. information load data MetaboLights database, refer vignette. type data loading, check xcms vignette specific vignette created data import soon. next configure parallel processing setup. functions xcms package allow per-sample parallel processing, can improve performance analysis, especially large data sets. xcms packages RforMassSpectrometry package ecosystem use parallel processing setup configured BiocParallel Bioconductor package. code use fork-based parallel processing unix system, socket-based parallel processing Windows operating system.","code":"param <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                            assayName = paste0(\"a_MTBLS8735_LC-MS_positive_\",                                               \"hilic_metabolite_profiling.txt\"),                            filePattern = \".mzML\")  lcms1 <- readMsObject(MsExperiment(),                       param,                       keepOntology = FALSE,                       keepProtocol = FALSE,                       simplify = TRUE) #' Set up parallel processing using 2 cores if (.Platform$OS.type == \"unix\") {     register(MulticoreParam(2)) } else {     register(SnowParam(2)) }"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-organisation","dir":"Articles","previous_headings":"","what":"Data organisation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"experimental data now represented MsExperiment object MsExperiment package. MsExperiment object container metadata spectral data provides manages also linkage samples spectra. provide brief overview data structure content. sampleData() function extracts sample information object. next extract data use pander package render show information Table 1 . Throughout document use R pipe operator (|>) avoid nested function calls hence improve code readability. sampleData() output MetaboLights always ideal direct easy access data. therefore rename transform user-friendly way. user can add, transform remove column want using base R functionalities. Table 1. Samples data set. Table 2. Simplified sample data. 11 samples data set. abbreviations essential proper interpretation metadata information: \"QC\": Quality control sample (pool serum samples external, large cohort). \"CVD\": Sample individual cardiovascular disease. \"CTR\": Sample presumably healthy control. sample_name: arbitrary name/identifier sample. age: (rounded) age individuals. injection_index: index representing order (position) individual sample measured (injected) within LC-MS measurement run experiment. define colors sample groups based sample group using RColorBrewer package: MS data experiment stored Spectra object (Spectra Bioconductor package) within MsExperiment object can accessed using spectra() function. element object spectrum - organised linearly combined Spectra object one (ordered retention time samples). access dataset’s Spectra object summarize available information provide, among things, total number spectra data set.","code":"lcms1 Object of class MsExperiment  Spectra: MS1 (17210)  Experiment data: 10 sample(s)  Sample data links:   - spectra: 10 sample(s) to 17210 element(s). sampleData(lcms1)[, c(\"Derived_Spectral_Data_File\",                       \"Characteristics[Sample type]\",                       \"Factor Value[Phenotype]\",                       \"Sample Name\",                       \"Factor Value[Age]\")] |>     kable(format = \"pipe\") # Let's rename the column for easier access colnames(sampleData(lcms1)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  # Add \"QC\" to the phenotype of the QC samples sampleData(lcms1)$phenotype[sampleData(lcms1)$sample_name == \"POOL\"] <- \"QC\" sampleData(lcms1)$sample_name[sampleData(lcms1)$sample_name == \"POOL\" ] <- c(\"POOL1\", \"POOL2\", \"POOL3\", \"POOL4\")  #  Add injection index column sampleData(lcms1)$injection_index <- seq_len(nrow(sampleData(lcms1)))  #let's look at the updated sample data sampleData(lcms1)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\",                      \"injection_index\")] |>     kable(format = \"pipe\") #' Define colors for the different phenotypes col_phenotype <- brewer.pal(9, name = \"Set1\")[c(9, 5, 4)] names(col_phenotype) <- c(\"QC\", # grey                           \"CVD\", # orange                           \"CTR\") # purple col_sample <- col_phenotype[sampleData(lcms1)$phenotype] #' Access Spectra Object spectra(lcms1) MSn data (Spectra) with 17210 spectra in a MsBackendMetaboLights backend:         msLevel     rtime scanIndex       <integer> <numeric> <integer> 1             1     0.274         1 2             1     0.553         2 3             1     0.832         3 4             1     1.111         4 5             1     1.390         5 ...         ...       ...       ... 17206         1   479.052      1717 17207         1   479.331      1718 17208         1   479.610      1719 17209         1   479.889      1720 17210         1   480.168      1721  ... 36 more variables/columns.  file(s): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML  ... 7 more files"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-visualization-and-general-quality-assessment","dir":"Articles","previous_headings":"","what":"Data visualization and general quality assessment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Effective visualization paramount inspecting assessing quality MS data. general overview LC-MS data, can: Combine mass peaks (MS1) spectra sample single spectrum mass peak represents maximum signal mass peaks similar m/z. spectrum might called Base Peak Spectrum (BPS), providing information abundant ions sample. Aggregate mass peak intensities spectrum, resulting Base Peak Chromatogram (BPC). BPC shows highest measured intensity distinct retention time (hence spectrum) thus orthogonal BPS. Sum mass peak intensities spectrum create Total Ion Chromatogram (TIC). Compare BPS samples experiment evaluate similarity ion content. Compare BPC samples experiment identify samples similar dissimilar chromatographic signal. addition general data evaluation visualization, also crucial investigate specific signal e.g. internal standards compounds/ions known present samples. providing reliable reference, internal standards help achieve consistent accurate analytical results. LC signal variable unstable nature LC, show BPC similarity. depth inspection discussion spectra chromatrograhic data cna found data exploration vignette. chromatogram() function facilitates extraction intensities along retention time. However, access chromatographic information currently efficient seamless spectral information. Work underway develop/improve infrastructure chromatographic data new Chromatograms object aimed flexible user-friendly Spectra object. visualizing LC-MS data, BPC TIC serves valuable tool assess performance liquid chromatography across various samples experiment. case, extract BPC data create plot. BPC captures maximum peak signal spectrum data file plots information retention time spectrum y-axis. BPC can extracted using chromatogram function. setting parameter aggregationFun = \"max\", instruct function report maximum signal per spectrum. Conversely, setting aggregationFun = \"sum\", sums intensities spectrum, thereby creating TIC.  Figure 5. BPC samples colored phenotype. 240 seconds signal seems measured. Thus, filter data removing part well first 10 seconds measured LC run.  Figure 6. BPC filtering retention time. Initially, examined entire BPC subsequently filtered based desired retention times. results smaller file size also facilitates straightforward interpretation BPC. final plot illustrates BPC sample colored phenotype, providing insights signal measured along retention times sample. reveals points compounds eluted LC column. essence, BPC condenses three-dimensional LC-MS data (m/z retention time intensity) two dimensions (retention time intensity). can also compare similarities BPCs heatmap. retention times however identical different samples. Thus bin() chromatographic signal per sample along retention time axis bins two seconds resulting data number bins/data points. can calculate pairwise similarities data vectors using cor() function visualize result using pheatmap().  Figure 7. Heatmap BPC similarities. heatmap reinforces exploration spectra data showed, strong separation QC study samples. important bear mind later analyses. Throughout entire process, crucial reference points within dataset, well-known ions. experiments nowadays include internal standards (), case . strongly recommend using visualization throughout entire analysis. experiment, set 15 spiked samples. reviewing signal , selected two guide analysis process. However, also advise plot evaluate ions steps. illustrate , generate Extracted Ion Chromatograms (EIC) selected test ions. restricting MS data intensities within restricted, small m/z range selected retention time window, EICs expected contain signal single type ion. expected m/z retention times set determined different experiment. Additionally, cases internal standards available, commonly present ions sample matrix can serve suitable alternatives. Ideally, compounds distributed across entire retention time range experiment. Table 4. Internal standard list respective m/z expected retention time [s]. plot EICs isotope labeled cystine methionine.  Figure 9. EIC cystine methionine. can observe clear concentration difference QCs study samples isotope labeled cystine ion. Meanwhile, labeled methionine internal standard exhibits discernible signal amidst noise noticeable retention time shift samples.","code":"#' Extract and plot BPC for full data bpc <- chromatogram(lcms1, aggregationFun = \"max\")  plot(bpc, col = paste0(col_sample, 80), main = \"BPC\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE,        bty = \"n\") #' Filter the data based on retention time lcms1 <- filterRt(lcms1, c(10, 240)) Filter spectra bpc <- chromatogram(lcms1, aggregationFun = \"max\")  #' Plot after filtering plot(bpc, col = paste0(col_sample, 80),      main = \"BPC after filtering retention time\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") #' Total ion chromatogram tic <- chromatogram(lcms1, aggregationFun = \"sum\") |>   bin(binSize = 2)  #' Calculate similarity (Pearson correlation) between BPCs ticmap <- do.call(cbind, lapply(tic, intensity)) |>   cor()  rownames(ticmap) <- colnames(ticmap) <- sampleData(lcms1)$sample_name ann <- data.frame(phenotype = sampleData(lcms1)[, \"phenotype\"]) rownames(ann) <- rownames(ticmap)  #' Plot heatmap pheatmap(ticmap, annotation_col = ann,          annotation_colors = list(phenotype = col_phenotype)) #' Load our list of standard intern_standard <- read.delim(\"intern_standard_list.txt\")  # Extract EICs for the list eic_is <- chromatogram(     lcms1,     rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),     mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")]))  #' Add internal standard metadata fData(eic_is)$mz <- intern_standard$mz fData(eic_is)$rt <- intern_standard$RT fData(eic_is)$name <- intern_standard$name fData(eic_is)$abbreviation <- intern_standard$abbreviation rownames(fData(eic_is)) <- intern_standard$abbreviation  #' Summary of IS information fData(eic_is)[, c(\"name\", \"mz\", \"rt\")] |>     kable(format = \"pipe\") #' Extract the two IS from the chromatogram object. eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"]  #' plot both EIC par(mfrow = c(1, 2), mar = c(4, 2, 2, 0.5)) plot(eic_cystine, main = fData(eic_cystine)$name, cex.axis = 0.8,      cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_cystine)$rt, col = \"red\", lty = 3)  plot(eic_met, main = fData(eic_met)$name, cex.axis = 0.8, cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_met)$rt, col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1,        bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"chromatographic-data-visualization-bpc-and-tic","dir":"Articles","previous_headings":"","what":"Chromatographic Data Visualization: BPC and TIC","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"chromatogram() function facilitates extraction intensities along retention time. However, access chromatographic information currently efficient seamless spectral information. Work underway develop/improve infrastructure chromatographic data new Chromatograms object aimed flexible user-friendly Spectra object. visualizing LC-MS data, BPC TIC serves valuable tool assess performance liquid chromatography across various samples experiment. case, extract BPC data create plot. BPC captures maximum peak signal spectrum data file plots information retention time spectrum y-axis. BPC can extracted using chromatogram function. setting parameter aggregationFun = \"max\", instruct function report maximum signal per spectrum. Conversely, setting aggregationFun = \"sum\", sums intensities spectrum, thereby creating TIC.  Figure 5. BPC samples colored phenotype. 240 seconds signal seems measured. Thus, filter data removing part well first 10 seconds measured LC run.  Figure 6. BPC filtering retention time. Initially, examined entire BPC subsequently filtered based desired retention times. results smaller file size also facilitates straightforward interpretation BPC. final plot illustrates BPC sample colored phenotype, providing insights signal measured along retention times sample. reveals points compounds eluted LC column. essence, BPC condenses three-dimensional LC-MS data (m/z retention time intensity) two dimensions (retention time intensity). can also compare similarities BPCs heatmap. retention times however identical different samples. Thus bin() chromatographic signal per sample along retention time axis bins two seconds resulting data number bins/data points. can calculate pairwise similarities data vectors using cor() function visualize result using pheatmap().  Figure 7. Heatmap BPC similarities. heatmap reinforces exploration spectra data showed, strong separation QC study samples. important bear mind later analyses.","code":"#' Extract and plot BPC for full data bpc <- chromatogram(lcms1, aggregationFun = \"max\")  plot(bpc, col = paste0(col_sample, 80), main = \"BPC\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE,        bty = \"n\") #' Filter the data based on retention time lcms1 <- filterRt(lcms1, c(10, 240)) Filter spectra bpc <- chromatogram(lcms1, aggregationFun = \"max\")  #' Plot after filtering plot(bpc, col = paste0(col_sample, 80),      main = \"BPC after filtering retention time\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") #' Total ion chromatogram tic <- chromatogram(lcms1, aggregationFun = \"sum\") |>   bin(binSize = 2)  #' Calculate similarity (Pearson correlation) between BPCs ticmap <- do.call(cbind, lapply(tic, intensity)) |>   cor()  rownames(ticmap) <- colnames(ticmap) <- sampleData(lcms1)$sample_name ann <- data.frame(phenotype = sampleData(lcms1)[, \"phenotype\"]) rownames(ann) <- rownames(ticmap)  #' Plot heatmap pheatmap(ticmap, annotation_col = ann,          annotation_colors = list(phenotype = col_phenotype))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"chromatographic-data-visualization-eics","dir":"Articles","previous_headings":"","what":"Chromatographic Data Visualization: EICs","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Throughout entire process, crucial reference points within dataset, well-known ions. experiments nowadays include internal standards (), case . strongly recommend using visualization throughout entire analysis. experiment, set 15 spiked samples. reviewing signal , selected two guide analysis process. However, also advise plot evaluate ions steps. illustrate , generate Extracted Ion Chromatograms (EIC) selected test ions. restricting MS data intensities within restricted, small m/z range selected retention time window, EICs expected contain signal single type ion. expected m/z retention times set determined different experiment. Additionally, cases internal standards available, commonly present ions sample matrix can serve suitable alternatives. Ideally, compounds distributed across entire retention time range experiment. Table 4. Internal standard list respective m/z expected retention time [s]. plot EICs isotope labeled cystine methionine.  Figure 9. EIC cystine methionine. can observe clear concentration difference QCs study samples isotope labeled cystine ion. Meanwhile, labeled methionine internal standard exhibits discernible signal amidst noise noticeable retention time shift samples.","code":"#' Load our list of standard intern_standard <- read.delim(\"intern_standard_list.txt\")  # Extract EICs for the list eic_is <- chromatogram(     lcms1,     rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),     mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")]))  #' Add internal standard metadata fData(eic_is)$mz <- intern_standard$mz fData(eic_is)$rt <- intern_standard$RT fData(eic_is)$name <- intern_standard$name fData(eic_is)$abbreviation <- intern_standard$abbreviation rownames(fData(eic_is)) <- intern_standard$abbreviation  #' Summary of IS information fData(eic_is)[, c(\"name\", \"mz\", \"rt\")] |>     kable(format = \"pipe\") #' Extract the two IS from the chromatogram object. eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"]  #' plot both EIC par(mfrow = c(1, 2), mar = c(4, 2, 2, 0.5)) plot(eic_cystine, main = fData(eic_cystine)$name, cex.axis = 0.8,      cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_cystine)$rt, col = \"red\", lty = 3)  plot(eic_met, main = fData(eic_met)$name, cex.axis = 0.8, cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_met)$rt, col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1,        bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-preprocessing","dir":"Articles","previous_headings":"","what":"Data preprocessing","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Preprocessing stands inaugural step analysis untargeted LC-MS. characterized 3 main stages: chromatographic peak detection, retention time shift correction (alignment) correspondence results features defined. primary objective preprocessing quantification signals ions measured sample, addressing potential retention time drifts samples, ensuring alignment quantified signals across samples within experiment. final result LC-MS data preprocessing numeric matrix abundances quantified entities samples experiment. initial preprocessing step involves detecting intensity peaks along retention time axis, called chromatographic peaks. achieve , employ findChromPeaks() function within xcms. function supports various algorithms peak detection, can selected configured respective parameter objects. preferred algorithm case, CentWave, utilizes continuous wavelet transformation (CWT)-based peak detection (Tautenhahn, Böttcher, Neumann 2008). method known effectiveness handling non-Gaussian shaped chromatographic peaks peaks varying retention time widths, commonly encountered HILIC separations. apply CentWave algorithm default settings extracted ion chromatogram cystine methionine ions evaluate results. CentWave highly performant algorithm, requires costumized dataset. implies parameters fine-tuned based user’s data. example serves clear motivation users familiarize various parameters need adapt data set. discuss main parameters can easily adjusted suit user’s dataset: peakwidth: Specifies minimal maximal expected width peaks retention time dimension. Highly dependent chromatographic settings used. ppm: maximal allowed difference mass peaks’ m/z values (parts-per-million) consecutive scans consider representing signal ion. integrate: parameter defines integration method. , primarily use integrate = 2 integrates also signal chromatographic peak’s tail considered accurate developers. determine peakwidth, recommend users refer previous EICs estimate range peak widths observe dataset. Ideally, examining multiple EICs goal. dataset, peak widths appear around 2 10 seconds. advise choosing range wide narrow peakwidth parameter can lead false positives negatives. determine ppm, deeper analysis dataset needed. clarified ppm depends instrument, users necessarily input vendor-advertised ppm. ’s determine accurately possible: following steps involve generating highly restricted MS area single mass peak per spectrum, representing cystine ion. m/z peaks extracted, absolute difference calculated finally expressed ppm. therefore, choose value close maximum within range parameter ppm, .e., 15 ppm. can now perform chromatographic peak detection adapted settings EICs. important note , properly estimate background noise, sufficient data points outside chromatographic peak need present. generally problem peak detection performed full LC-MS data set, peak detection EICs retention time range EIC needs sufficiently wide. function fails find peak EIC, initial troubleshooting step increase range. Additionally, signal--noise threshold snthresh reduced peak detection EICs, within small retention time range, enough signal present properly estimate background noise. Finally, case MS1 data points per peaks, setting CentWave’s advanced parameter extendLengthMSW TRUE can help peak detection. customized parameters, chromatographic peak detected sample. , use plot() function EICs visualize results.  Figure 11. Chromatographic peak detection EICs. can see peak seems ot detected sample ions. indicates custom settings seem thus suitable dataset. now proceed apply entire dataset, extracting EICs ions evaluate confirm chromatographic peak detection worked expected. Note: revert value parameter snthresh default, , mentioned , background noise estimation reliable performed full data set. Parameter chunkSize findChromPeaks() defines number data files loaded memory processed simultaneously. parameter thus allows fine-tune memory demand well performance chromatographic peak detection step. plot EICs two selected internal standards evaluate chromatographic peak detection results.  Figure 12. Chromatographic peak detection EICs processing. Peaks seem detected properly samples ions. indicates peak detection process entire dataset successful. identification chromatographic peaks using CentWave algorithm can sometimes result artifacts, overlapping split peaks. address issue, refineChromPeaks() function utilized, conjunction MergeNeighboringPeaksParam, aims merging split peaks. show examples CentWave peak detection artifacts. examples pre-selected illustrate necessity next step:  Figure 13. Examples CentWave peak detection artifacts. cases signal presumably single type ion split two separate chromatographic peaks (indicated vertical line). MergeNeigboringPeaksParam allows combine split peaks. parameters algorithm defined : expandMz: Suggested kept relatively small (0.0015) prevent merging isotopes. expandRt: Usually set approximately half size average retention time width used chromatographic peak detection (case, 2.5 seconds). minProp: Used determine whether candidates merged. Chromatographic peaks overlapping m/z ranges (expanded side expandMz) tail--head distance retention time dimension less 2 * expandRt, signal higher minProp apex intensity chromatographic peak lower intensity, merged. Values parameter small avoid merging closely co-eluting ions, isomers. test settings EICs split peaks.  Figure 14. Examples CentWave peak detection artifacts merging. can observe artificially split peaks appropriately merged. Therefore, next apply settings entire dataset. peak merging, column \"merged\" result object’s chromPeakData() data frame can used evaluate chromatographic peaks result represent signal merged, originally identified chromatographic peaks. proceeding next preprocessing step generally suggested evaluate results chromatographic peak detection EICs e.g. internal standards compounds/ions known present samples. Additionally, evaluating comparing number identified chromatographic peaks samples data set can help spotting potentially problematic samples. count number chromatographic peaks per sample show numbers table. Table 5. Samples number identified chromatographic peaks. similar number chromatographic peaks identified within various samples data set. Additional options evaluate results chromatographic peak detection can implemented using plotChromPeaks() function summarizing results using base R commands. Despite using chromatographic settings conditions retention time shifts unavoidable. Indeed, performance instrument can change time, example due small variations environmental conditions, temperature pressure. shifts generally small samples measured within batch/measurement run, can considerable data experiment acquired across longer time period. evaluate presence shift extract plot BPC QC samples.  Figure 15. BPC QC samples. QC samples representing sample (pool) measured regular intervals measurement run experiment measured day. Still, small shifts can observed, especially region 100 150 seconds. facilitate proper correspondence signals across samples (hence definition LC-MS features), essential minimize differences retention times. Theoretically, proceed two steps: first select QC samples dataset first alignment , using -called anchor peaks. way can assume linear shift time, since always measuring sample different regular time intervals. Despite external QCs data set, still use subset-based alignment assuming retention time shifts independent different sample matrix (human serum plasma) instead mostly instrument-dependent. Note also possible manually specify anchor peaks, respectively retention times align data set external, reference, data set. information provided vignettes xcms package. calculating much adjust retention time samples, apply shift also study samples. xcms retention time alignment can performed using adjustRtime() function alignment algorithm. example use PeakGroups method (Smith et al. 2006) performs alignment minimizing differences retention times set anchor peaks different samples. method requires initial correspondence analysis match/group chromatographic peaks across samples algorithm selects anchor peaks alignment. initial correspondence, use PeakDensity approach (Smith et al. 2006) groups chromatographic peaks similar m/z retention time LC-MS features. parameters algorithm, can configured using PeakDensityParam object, sampleGroups, minFraction, binSize, ppm bw. binSize, ppm bw allow specify similar chromatographic peaks’ m/z retention time values need consider grouping feature. binSize ppm define required similarity m/z values. Within m/z bin (defined binSize ppm) areas along retention time axis high chromatographic peak density (considering peaks samples) identified, chromatographic peaks within regions considered grouping feature. High density areas identified using base R density() function, bw parameter: higher values define wider retention time areas, lower values require chromatographic peaks similar retention times. parameter can seen black line plot , corresponding smoothness density curve. Whether candidate peaks get grouped feature depends also parameters sampleGroups minFraction: sampleGroups provide, sample, sample group belongs . minFraction expected value 0 1 defining proportion samples within least one sample groups (defined sampleGroups) chromatographic peaks detected group feature. initial correspondence, parameters don’t need fully optimized. Selection dataset-specific parameter values described detail next section. dataset, use small values binSize ppm , importantly, also parameter bw, since data set ultra high performance (UHP) LC setup used. minFraction use high value (0.9) ensure features defined chromatographic peaks present almost samples one sample group (can used anchor peaks actual alignment). base alignment later QC samples hence define sampleGroups binary variable grouping samples either study, QC group.  Figure 16. Initial correspondence analysis. PeakGroups-based alignment can next performed using adjustRtime() function PeakGroupsParam parameter object. parameters algorithm : subsetAdjust subset: Allows subset alignment. base retention time alignment QC samples, .e., retention time shifts estimated based repeatedly measured samples. resulting adjustment applied entire data. data sets QC samples (e.g. sample pools) measured repeatedly, strongly suggest use method. Note also subset-based alignment samples ordered injection index (.e., order measured measurement run). minFraction: value 0 1 defining proportion samples (full data set, data subset defined subset) chromatographic peak identified use anchor peak. contrast PeakDensityParam parameter used define proportion within sample group. span: PeakGroups method allows, depending data, adjust regions along retention time axis differently. enable local alignments LOESS function used parameter defines degree smoothing function. Generally, values 0.4 0.6 used, however, suggested evaluate alignment results eventually adapt parameters result satisfactory. perform alignment data set based retention times anchor peaks defined subset QC samples. Alignment adjusted retention times spectra data set, well retention times identified chromatographic peaks. alignment performed, user evaluate results using plotAdjustedRtime() function. function visualizes difference adjusted raw retention time sample y-axis along adjusted retention time x-axis. Dot points represent position used anchor peak along retention time axis. optimal alignment areas along retention time axis, anchor peaks scattered retention time dimension.  Figure 17. Retention time alignment results. samples present data set measured within measurement run, resulting small retention time shifts. Therefore, little adjustments needed performed (shifts maximum 1 second can seen plot ). Generally, magnitude adjustment seen plots match expectation analyst. can also compare BPC alignment. get original data, .e. raw retention times, can use dropAdjustedRtime() function:  Figure 18. BPC alignment. largest shift can observed retention time range 120 130s. Apart retention time range, little changes can observed. next evaluate impact alignment EICs selected internal standards. thus first extract ion chromatograms alignment. can now evaluate alignment effect test ions. plot EICs alignment isotope labeled cystine methionine.  Figure 19. EICs cystine methionine alignment. non-endogenous cystine ion already well aligned difference minimal. methionine ion, however, shows improvement alignment. addition visual inspection results, also evaluate impact alignment comparing variance retention times internal standards alignment. end, first need identify chromatographic peaks sample m/z retention time close expected values internal standard. use matchValues() function MetaboAnnotation package (Rainer et al. 2022) using MzRtParam method identify chromatographic peaks similar m/z (+/- 50 ppm) retention time (+/- 10 seconds) internal standard’s values. parameters mzColname rtColname specify column names query () target (chromatographic peaks) contain m/z retention time values match entities. perform matching separately sample. internal standard every sample, use filterMatches() function SingleMatchParam() parameter select chromatographic peak highest intensity. now internal standard ID chromatographic peak sample likely represents signal ion. can now extract retention times chromatographic peaks alignment. can now evaluate impact alignment retention times internal standards across full data set:  Figure 20. Retention time variation internal standards alignment. average, variation retention times internal standards across samples slightly reduced alignment. briefly touched subject correspondence determine anchor peaks alignment. Generally, goal correspondence analysis identify chromatographic peaks originate types ions samples experiment group LC-MS features. point, proper configuration parameter bw crucial. illustrate sensible choices parameter’s value can made. use plotChromPeakDensity() function simulate correspondence analysis default values PeakGroups extracted ion chromatograms two selected isotope labeled ions. plot shows EIC top panel, apex position chromatographic peaks different samples (y-axis), along retention time (x-axis) lower panel.  Figure 21. Initial correspondence analysis, Cystine.  Figure 22. Initial correspondence analysis, Methionine. Grouping peaks depends smoothness previousl mentionned density curve can configured parameter bw. seen , smoothness high properly group features. looking default parameters, can observe indeed, bw parameter set bw = 30, high modern UHPLC-MS setups. reduce value parameter 1.8 evaluate impact.  Figure 23. Correspondence analysis optimized parameters, Cystine.  Figure 24. Correspondence analysis optimized parameters, Methionine. can observe peaks now grouped accurately single feature test ion. important parameters optimized : binsize: data generated high resolution MS instrument, thus select low value paramete. ppm: TOF instruments, suggested use value ppm larger 0 accommodate higher measurement error instrument larger m/z values. minFraction: set minFraction = 0.75, hence defining features chromatographic peak identified least 75% samples one sample groups. sampleGroups: use information available sampleData’s \"phenotype\" column. correspondence analysis suggested evaluate results selected EICs. extract signal m/z similar isotope labeled methionine larger retention time range. Importantly, show actual correspondence results, set simulate = FALSE plotChromPeakDensity() function.  Figure 25. Correspondence analysis results, Methionine. hoped, signal two different ions now grouped separate features. Generally, correspondence results evaluated extracted chromatograms. Another interesting information look distribution features along retention time axis. results correspondence analysis now stored, along results preprocessing steps, within XcmsExperiment result object. correspondence results, .e., definition LC-MS features, can extracted using featureDefinitions() function. data frame provides average m/z retention time (columns \"mzmed\" \"rtmed\") characterize LC-MS feature. Column, \"peakidx\" contains indices chromatographic peaks assigned feature. actual abundances features, represent also final preprocessing results, can extracted featureValues() function: can note features (e.g. F0003 F0006) missing values samples. expected certain degree samples features, respectively ions, need present. address next section. previously observed missing values (NA) attributed various reasons. Although might represent genuinely missing value, indicating ion (feature) truly present particular sample, also result failure preceding chromatographic peak detection step. crucial able recover missing values latter category much possible reduce eventual need data imputation. next examine prevalent missing values present dataset: can observe substantial number missing values values dataset. Let’s therefore delve process gap-filling. first evaluate example features chromatographic peak detected samples:  Figure 26. Examples chromatographic peaks missing values. instances, chromatographic peak identified one two selected samples (red line), hence missing value reported feature particular samples (blue line). However, cases, signal measured samples, thus, reporting missing value correct example. signal feature low, likely reason peak detection failed. rescue signal cases, fillChromPeaks() function can used ChromPeakAreaParam approach. method defines m/z-retention time area feature based detected peaks, signal respective ion expected. integrates intensities within area samples missing values feature. reported feature abundance. apply method using default parameters. fillChromPeaks() thus rescue missing data data set. Note , even sample ion present, worst case noise integrated, expected much lower actual chromatographic peak signal. Let’s look previously missing values :  Figure 27. Examples chromatographic peaks missing values gap-filling. gap-filling, also blue colored sample chromatographic peak present peak area reported feature abundance sample. assess effectiveness gap-filling method rescuing signals, can also plot average signal features least one missing value average filled-signal. advisable perform analysis repeatedly measured samples; case, QC samples used. , extract: Feature values detected chromatographic peaks setting filled = FALSE featuresValues() call. filled-signal first extracting detected gap-filled abundances replace values detected chromatographic peaks NA. , calculate row averages matrices plot .  detected (x-axis) gap-filled (y-axis) values QC samples highly correlated. Especially higher abundances, agreement high, low intensities, can expected, differences higher trending correlation line. , addition, fit linear regression line data summarize results linear regression line slope 1.12 intercept -1.62. indicates filled-signal average 1.12 times higher detected signal. final results LC-MS data preprocessing stored within XcmsExperiment object. includes identified chromatographic peaks, alignment results, well correspondence results. addition, guarantee reproducibility, result object keeps track performed processing steps, including individual parameter objects used configure . processHistory() function returns list various applied processing steps chronological order. , extract information first step performed preprocessing. processParam() function used extract actual parameter class used configure processing step. final result whole LC-MS data preprocessing two-dimensional matrix abundances -called LC-MS features samples. Note stage analysis features characterized m/z retention time don’t yet information metabolite feature represent. seen , feature matrix can extracted featureValues() function corresponding feature characteristics (.e., m/z retention time values) using featureDefinitions() function. Thus, two arrays extracted xcms result object used/imported analysis packages processing. example also exported tab delimited text files, used external tool, used, also MS2 spectra available, feature-based molecular networking GNPS analysis environment (Nothias et al. 2020). processing R, reference link raw MS data required, suggested extract xcms preprocessing result using quantify() function SummarizedExperiment object, Bioconductor’s default container data biological assays/experiments. simplifies integration Bioconductor analysis packages. quantify() function takes parameters featureValues() function, thus, call extract SummarizedExperiment detected, gap-filled, feature abundances: Sample identifications xcms result’s sampleData() now available colData() (column, sample annotations) featureDefinitions() rowData() (row, feature annotations). feature values added first assay() SummarizedExperiment even processing history available object’s metadata(). SummarizedExperiment supports multiple assays, numeric matrices dimensions. thus add detected gap-filled feature abundances additional assay SummarizedExperiment. Feature abundances can extracted assay() function. extract first 6 lines detected gap-filled feature abundances: advantage, addition container full preprocessing results also possibility easy intuitive creation data subsets ensuring data integrity. example easy subset full data selection features /samples: moving next step analysis, advisable save preprocessing results. multiple format options save , can found MsIO package documentation. save XcmsExperiment object file format handled alabster framework, ensures object can easily read languages like Python Javascript well loaded easily back R.","code":"#' Use default Centwave parameter param <- CentWaveParam()  #' Look at the default parameters param Object of class:  CentWaveParam  Parameters:  - ppm: [1] 25  - peakwidth: [1] 20 50  - snthresh: [1] 10  - prefilter: [1]   3 100  - mzCenterFun: [1] \"wMean\"  - integrate: [1] 1  - mzdiff: [1] -0.001  - fitgauss: [1] FALSE  - noise: [1] 0  - verboseColumns: [1] FALSE  - roiList: list()  - firstBaselineCheck: [1] TRUE  - roiScales: numeric(0)  - extendLengthMSW: [1] FALSE  - verboseBetaColumns: [1] FALSE #' Evaluate for Cystine cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt rtmin rtmax into intb maxo sn row column #' Evaluate for Methionine met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt rtmin rtmax into intb maxo sn row column #' Restrict the data to signal from cystine in the first sample cst <- lcms1[1L] |>   spectra() |>   filterRt(rt = c(208, 218)) |>   filterMzRange(mz = fData(eic_cystine)[\"cystine_13C_15N\", c(\"mzmin\", \"mzmax\")])  #' Show the number of peaks per m/z filtered spectra lengths(cst) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 #' Calculate the difference in m/z values between scans mz_diff <- cst |>     mz() |>     unlist() |>     diff() |>     abs()  #' Express differences in ppm range(mz_diff * 1e6 / mean(unlist(mz(cst)))) [1]  0.08829605 14.82188728 #' Parameters adapted for chromatographic peak detection on EICs. param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2,                        snthresh = 2)  #' Evaluate on the cystine ion cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt   rtmin   rtmax      into      intb      maxo sn row column  [1,] 209.251 207.577 212.878  4085.675  2911.376  2157.459  4   1      1  [2,] 209.251 206.182 213.995 24625.728 19074.407 12907.487  4   1      2  [3,] 209.252 207.020 214.274 19467.836 14594.041  9996.466  4   1      3  [4,] 209.251 207.577 212.041  4648.229  3202.617  2458.485  3   1      4  [5,] 208.974 206.184 213.159 23801.825 18126.978 11300.289  3   1      5  [6,] 209.250 207.018 213.714 25990.327 21036.768 13650.329  5   1      6  [7,] 209.252 207.857 212.879  4528.767  3259.039  2445.841  4   1      7  [8,] 209.252 207.299 213.995 23119.449 17274.140 12153.410  4   1      8  [9,] 208.972 206.740 212.878 28943.188 23436.119 14451.023  4   1      9 [10,] 209.252 207.578 213.437  4470.552  3065.402  2292.881  4   1     10 #' Evaluate on the methionine ion met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt   rtmin   rtmax     into     intb      maxo sn row column  [1,] 159.867 157.913 162.378 20026.61 14715.42 12555.601  4   1      1  [2,] 160.425 157.077 163.215 16827.76 11843.39  8407.699  3   1      2  [3,] 160.425 157.356 163.215 18262.45 12881.67  9283.375  3   1      3  [4,] 159.588 157.635 161.820 20987.72 15424.25 13327.811  4   1      4  [5,] 160.985 156.799 163.217 16601.72 11968.46 10012.396  4   1      5  [6,] 160.982 157.634 163.214 17243.24 12389.94  9150.079  4   1      6  [7,] 159.867 158.193 162.099 21120.10 16202.05 13531.844  3   1      7  [8,] 160.426 157.356 162.937 18937.40 13739.73 10336.000  3   1      8  [9,] 160.704 158.472 163.215 17882.21 12299.43  9395.548  3   1      9 [10,] 160.146 157.914 162.379 20275.80 14279.50 12669.821  3   1     10 #' Using the same settings, but with default snthresh param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms1 <- findChromPeaks(lcms1, param = param, chunkSize = 5)  #' Update EIC internal standard object eics_is_noprocess <- eic_is eic_is <- chromatogram(lcms1,,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) Processing chromatographic peaks fData(eic_is) <- fData(eics_is_noprocess) Processing chromatographic peaks #' set up the parameter param <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75)  #' Perform the peak refinement on the EICs eics <- refineChromPeaks(eics, param = param) plot(eics) #' Apply on whole dataset lcms1 <- refineChromPeaks(lcms1, param = param, chunkSize = 5) Reduced from 106714 to 89182 chromatographic peaks. chromPeakData(lcms1)$merged |>                       table() FALSE  TRUE 79908  9274 eics_is_chrompeaks <- eic_is  eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_chrompeaks)  eic_cystine <- eic_is[\"cystine_13C_15N\", ] eic_met <- eic_is[\"methionine_13C_15N\", ] #' Count the number of peaks per sample and summarize them in a table. data.frame(sample_name = sampleData(lcms1)$sample_name,            peak_count = as.integer(table(chromPeaks(lcms1)[, \"sample\"]))) |>     kable(format = \"pipe\") #' Get QC samples QC_samples <- sampleData(lcms1)$phenotype == \"QC\"  #' extract BPC lcms1[QC_samples] |>     chromatogram(aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(col = col_phenotype[\"QC\"], main = \"BPC of QC samples\") |>     grid() # Initial correspondence analysis param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype == \"QC\",                           minFraction = 0.9,                           binSize = 0.01, ppm = 10,                           bw = 2) lcms1 <- groupChromPeaks(lcms1, param = param)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) #' Define parameters of choice subset <- which(sampleData(lcms1)$phenotype == \"QC\") param <- PeakGroupsParam(minFraction = 0.9, extraPeaks = 50, span = 0.5,                          subsetAdjust = \"average\",                          subset = subset)  #' Perform the alignment lcms1 <- adjustRtime(lcms1, param = param) #' Visualize alignment results plotAdjustedRtime(lcms1, col = paste0(col_sample, 80), peakGroupsPch = 1) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\") #' Get data before alignment lcms1_raw <- dropAdjustedRtime(lcms1)  #' Apply the adjusted retention time to our dataset lcms1 <- applyAdjustedRtime(lcms1) #' Plot the BPC before and after alignment par(mfrow = c(2, 1), mar = c(2, 1, 1, 0.5)) chromatogram(lcms1_raw, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC before alignment\", col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE)  chromatogram(lcms1, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC after alignment\",          col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE) #' Store the EICs before alignment eics_is_refined <- eic_is  #' Update the EICs eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_refined)  #' Extract the EICs for the test ions eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"] par(mfrow = c(2, 2), mar = c(4, 4.5, 2, 1))  old_eic_cystine <- eics_is_refined[\"cystine_13C_15N\"] plot(old_eic_cystine, main = \"Cystine before alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  old_eic_met <- eics_is_refined[\"methionine_13C_15N\"] plot(old_eic_met, main = \"Methionine before alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  plot(eic_cystine, main = \"Cystine after alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\")  plot(eic_met, main = \"Methionine after alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3) #' Extract the matrix with all chromatographic peaks and add a column #' with the ID of the chromatographic peak chrom_peaks <- chromPeaks(lcms1) |> as.data.frame() chrom_peaks$peak_id <- rownames(chrom_peaks)  #' Define the parameters for the matching and filtering of the matches p_1 <- MzRtParam(ppm = 50, toleranceRt = 10) p_2 <- SingleMatchParam(duplicates = \"top_ranked\", column = \"target_maxo\",                         decreasing = TRUE)  #' Iterate over samples and identify for each the chromatographic peaks #' with similar m/z and retention time than the onse from the internal #' standard, and extract among them the ID of the peaks with the #' highest intensity. intern_standard_peaks <- lapply(seq_along(lcms1), function(i) {     tmp <- chrom_peaks[chrom_peaks[, \"sample\"] == i, , drop = FALSE]     mtch <- matchValues(intern_standard, tmp,                         mzColname = c(\"mz\", \"mz\"),                         rtColname = c(\"RT\", \"rt\"),                         param = p_1)     mtch <- filterMatches(mtch, p_2)     mtch$target_peak_id }) |>     do.call(what = cbind) #' Define the index of the selected chromatographic peaks in the #' full chromPeaks matrix idx <- match(intern_standard_peaks, rownames(chromPeaks(lcms1)))  #' Extract the raw retention times for these rt_raw <- chromPeaks(lcms1_raw)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw))  #' Extract the adjusted retention times for these rt_adj <- chromPeaks(lcms1)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw)) list(all_raw = rowSds(rt_raw, na.rm = TRUE),      all_adj = rowSds(rt_adj, na.rm = TRUE)      ) |>     vioplot() grid() #' Default parameter for the grouping and apply them to the test ions BPC param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 30)  param Object of class:  PeakDensityParam  Parameters:  - sampleGroups:  [1] \"QC\"  \"CVD\" \"CTR\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  - bw: [1] 30  - minFraction: [1] 0.5  - minSamples: [1] 1  - binSize: [1] 0.25  - maxFeatures: [1] 50  - ppm: [1] 0 plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Updating parameters param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 1.8)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Define the settings for the param param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype,                           minFraction = 0.75, binSize = 0.01, ppm = 10,                           bw = 1.8)  #' Apply to whole data lcms1 <- groupChromPeaks(lcms1, param = param) #' Extract chromatogram for an m/z similar to the one of the labeled methionine chr_test <- chromatogram(lcms1,                          mz = as.matrix(intern_standard[\"methionine_13C_15N\",                                                         c(\"mzmin\", \"mzmax\")]),                          rt = c(145, 200),                          aggregationFun = \"max\") plotChromPeakDensity(     chr_test, simulate = FALSE,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(chr_test)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(chr_test)[, \"sample\"]], 20),     peakPch = 16) # Bin features per RT slices vc <- featureDefinitions(lcms1)$rtmed breaks <- seq(0, max(vc, na.rm = TRUE) + 1, length.out = 15) |>     round(0) cuts <- cut(vc, breaks = breaks, include.lowest = TRUE)  table(cuts) cuts    [0,17]   (17,34]   (34,51]   (51,68]   (68,86]  (86,103] (103,120] (120,137]        15      2608       649        24       132        11        15       105 (137,154] (154,171] (171,188] (188,205] (205,222] (222,240]      1441      2202       410       869       554        33 #' Definition of the features featureDefinitions(lcms1) |>   head() mzmed    mzmin    mzmax    rtmed    rtmin    rtmax npeaks CTR CVD QC FT0001 50.98979 50.98949 50.99038 203.6001 203.1181 204.2331      8   1   3  4 FT0002 51.05904 51.05880 51.05941 191.1675 190.8787 191.5050      9   2   3  4 FT0003 51.98657 51.98631 51.98699 203.1467 202.6406 203.6710      7   0   3  4 FT0004 53.02036 53.02009 53.02043 203.2343 202.5652 204.0901     10   3   3  4 FT0005 53.52080 53.52051 53.52102 203.1936 202.8490 204.0901     10   3   3  4 FT0006 54.01007 54.00988 54.01015 159.2816 158.8499 159.4484      6   1   3  2             peakidx ms_level FT0001 7702, 16....        1 FT0002 7176, 16....        1 FT0003 7680, 17....        1 FT0004 7763, 17....        1 FT0005 8353, 17....        1 FT0006 5800, 15....        1 #' Extract feature abundances featureValues(lcms1, method = \"sum\") |>     head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422            NA              481.7436 FT0002              710.8078      875.9192            NA              693.6997 FT0003              445.5711      613.4410            NA              497.8866 FT0004            16994.5260    24605.7340     19766.707            17808.0933 FT0005             3284.2664     4526.0531      3521.822             3379.8909 FT0006            10681.7476    10009.6602            NA            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001            NA      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003            NA      634.9370              449.0933            NA FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006            NA     7296.4262                    NA     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785                    NA #' Percentage of missing values sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 26.41597 ftidx <- which(is.na(rowSums(featureValues(lcms1)))) fts <- rownames(featureDefinitions(lcms1))[ftidx] farea <- featureArea(lcms1, features = fts[1:2])  chromatogram(lcms1[c(2, 3)],              rt = farea[, c(\"rtmin\", \"rtmax\")],              mz = farea[, c(\"mzmin\", \"mzmax\")]) |>     plot(col = c(\"red\", \"blue\"), lwd = 2) #' Fill in the missing values in the whole dataset lcms1 <- fillChromPeaks(lcms1, param = ChromPeakAreaParam(), chunkSize = 5)  #' Percentage of missing values after gap-filling sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 5.155492 #' Get only detected signal in QC samples vals_detect <- featureValues(lcms1, filled = FALSE)[, QC_samples]  #' Get detected and filled-in signal vals_filled <- featureValues(lcms1)[, QC_samples]  #' Replace detected signal with NA vals_filled[!is.na(vals_detect)] <- NA  #' Identify features with at least one filled peak has_filled <- is.na(rowSums(vals_detect))  #' Calculate row averages for features with missing values avg_detect <- rowMeans(vals_detect[has_filled, ], na.rm = TRUE) avg_filled <- rowMeans(vals_filled[has_filled, ], na.rm = TRUE)  #' Plot the values against each other (in log2 scale) plot(log2(avg_detect), log2(avg_filled),      xlim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      ylim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      pch = 21, bg = \"#00000020\", col = \"#00000080\") grid() abline(0, 1) #' fit a linear regression line to the data l <- lm(log2(avg_filled) ~ log2(avg_detect)) summary(l) Call: lm(formula = log2(avg_filled) ~ log2(avg_detect))  Residuals:     Min      1Q  Median      3Q     Max -6.8176 -0.3807  0.1725  0.5492  6.7504  Coefficients:                  Estimate Std. Error t value Pr(>|t|) (Intercept)      -1.62359    0.11545  -14.06   <2e-16 *** log2(avg_detect)  1.11763    0.01259   88.75   <2e-16 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  Residual standard error: 0.9366 on 2846 degrees of freedom   (846 observations deleted due to missingness) Multiple R-squared:  0.7346,    Adjusted R-squared:  0.7345 F-statistic:  7877 on 1 and 2846 DF,  p-value: < 2.2e-16 #' Check first step of the process history processHistory(lcms1)[[1]] Object of class \"XProcessHistory\"  type: Peak detection  date: Wed Oct 30 16:17:12 2024  info:  fileIndex: 1,2,3,4,5,6,7,8,9,10  Parameter class: CentWaveParam  MS level(s) 1 #' Extract results as a SummarizedExperiment res <- quantify(lcms1, method = \"sum\", filled = FALSE) res class: SummarizedExperiment dim: 9068 10 metadata(6): '' '' ... '' '' assays(1): raw rownames(9068): FT0001 FT0002 ... FT9067 FT9068 rowData names(11): mzmed mzmin ... QC ms_level colnames(10): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML ... MS_F_POS.mzML   MS_QC_POOL_4_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index assays(res)$raw_filled <- featureValues(lcms1, method = \"sum\",                                         filled = TRUE )  #' Different assay in the SummarizedExperiment object assayNames(res) [1] \"raw\"        \"raw_filled\" assay(res, \"raw_filled\") |> head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422      411.3295              481.7436 FT0002              710.8078      875.9192      457.5920              693.6997 FT0003              445.5711      613.4410      277.5022              497.8866 FT0004            16994.5260    24605.7340    19766.7069            17808.0933 FT0005             3284.2664     4526.0531     3521.8221             3379.8909 FT0006            10681.7476    10009.6602     9599.9701            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001      314.7567      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003      425.3774      634.9370              449.0933      556.2544 FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006     4792.2390     7296.4262             2382.1788     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785             6911.5439 res[1:14, 3:8] class: SummarizedExperiment dim: 14 6 metadata(6): '' '' ... '' '' assays(2): raw raw_filled rownames(14): FT0001 FT0002 ... FT0013 FT0014 rowData names(11): mzmed mzmin ... QC ms_level colnames(6): MS_B_POS.mzML MS_QC_POOL_2_POS.mzML ...   MS_QC_POOL_3_POS.mzML MS_E_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index #' XcmsExperiment object:  saveMsObject(lcms1,              AlabasterParam(path = file.path(\"objects/preprocessed_lcms1\")))  #' Below we remove the processHistory of the res object to allow for export,  #' this need to be fixed later. metadata(res) <- list() saveObject(res, file.path(\"objects/preprocessed_res\"))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"chromatographic-peak-detection","dir":"Articles","previous_headings":"","what":"Chromatographic peak detection","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"initial preprocessing step involves detecting intensity peaks along retention time axis, called chromatographic peaks. achieve , employ findChromPeaks() function within xcms. function supports various algorithms peak detection, can selected configured respective parameter objects. preferred algorithm case, CentWave, utilizes continuous wavelet transformation (CWT)-based peak detection (Tautenhahn, Böttcher, Neumann 2008). method known effectiveness handling non-Gaussian shaped chromatographic peaks peaks varying retention time widths, commonly encountered HILIC separations. apply CentWave algorithm default settings extracted ion chromatogram cystine methionine ions evaluate results. CentWave highly performant algorithm, requires costumized dataset. implies parameters fine-tuned based user’s data. example serves clear motivation users familiarize various parameters need adapt data set. discuss main parameters can easily adjusted suit user’s dataset: peakwidth: Specifies minimal maximal expected width peaks retention time dimension. Highly dependent chromatographic settings used. ppm: maximal allowed difference mass peaks’ m/z values (parts-per-million) consecutive scans consider representing signal ion. integrate: parameter defines integration method. , primarily use integrate = 2 integrates also signal chromatographic peak’s tail considered accurate developers. determine peakwidth, recommend users refer previous EICs estimate range peak widths observe dataset. Ideally, examining multiple EICs goal. dataset, peak widths appear around 2 10 seconds. advise choosing range wide narrow peakwidth parameter can lead false positives negatives. determine ppm, deeper analysis dataset needed. clarified ppm depends instrument, users necessarily input vendor-advertised ppm. ’s determine accurately possible: following steps involve generating highly restricted MS area single mass peak per spectrum, representing cystine ion. m/z peaks extracted, absolute difference calculated finally expressed ppm. therefore, choose value close maximum within range parameter ppm, .e., 15 ppm. can now perform chromatographic peak detection adapted settings EICs. important note , properly estimate background noise, sufficient data points outside chromatographic peak need present. generally problem peak detection performed full LC-MS data set, peak detection EICs retention time range EIC needs sufficiently wide. function fails find peak EIC, initial troubleshooting step increase range. Additionally, signal--noise threshold snthresh reduced peak detection EICs, within small retention time range, enough signal present properly estimate background noise. Finally, case MS1 data points per peaks, setting CentWave’s advanced parameter extendLengthMSW TRUE can help peak detection. customized parameters, chromatographic peak detected sample. , use plot() function EICs visualize results.  Figure 11. Chromatographic peak detection EICs. can see peak seems ot detected sample ions. indicates custom settings seem thus suitable dataset. now proceed apply entire dataset, extracting EICs ions evaluate confirm chromatographic peak detection worked expected. Note: revert value parameter snthresh default, , mentioned , background noise estimation reliable performed full data set. Parameter chunkSize findChromPeaks() defines number data files loaded memory processed simultaneously. parameter thus allows fine-tune memory demand well performance chromatographic peak detection step. plot EICs two selected internal standards evaluate chromatographic peak detection results.  Figure 12. Chromatographic peak detection EICs processing. Peaks seem detected properly samples ions. indicates peak detection process entire dataset successful. identification chromatographic peaks using CentWave algorithm can sometimes result artifacts, overlapping split peaks. address issue, refineChromPeaks() function utilized, conjunction MergeNeighboringPeaksParam, aims merging split peaks. show examples CentWave peak detection artifacts. examples pre-selected illustrate necessity next step:  Figure 13. Examples CentWave peak detection artifacts. cases signal presumably single type ion split two separate chromatographic peaks (indicated vertical line). MergeNeigboringPeaksParam allows combine split peaks. parameters algorithm defined : expandMz: Suggested kept relatively small (0.0015) prevent merging isotopes. expandRt: Usually set approximately half size average retention time width used chromatographic peak detection (case, 2.5 seconds). minProp: Used determine whether candidates merged. Chromatographic peaks overlapping m/z ranges (expanded side expandMz) tail--head distance retention time dimension less 2 * expandRt, signal higher minProp apex intensity chromatographic peak lower intensity, merged. Values parameter small avoid merging closely co-eluting ions, isomers. test settings EICs split peaks.  Figure 14. Examples CentWave peak detection artifacts merging. can observe artificially split peaks appropriately merged. Therefore, next apply settings entire dataset. peak merging, column \"merged\" result object’s chromPeakData() data frame can used evaluate chromatographic peaks result represent signal merged, originally identified chromatographic peaks. proceeding next preprocessing step generally suggested evaluate results chromatographic peak detection EICs e.g. internal standards compounds/ions known present samples. Additionally, evaluating comparing number identified chromatographic peaks samples data set can help spotting potentially problematic samples. count number chromatographic peaks per sample show numbers table. Table 5. Samples number identified chromatographic peaks. similar number chromatographic peaks identified within various samples data set. Additional options evaluate results chromatographic peak detection can implemented using plotChromPeaks() function summarizing results using base R commands.","code":"#' Use default Centwave parameter param <- CentWaveParam()  #' Look at the default parameters param Object of class:  CentWaveParam  Parameters:  - ppm: [1] 25  - peakwidth: [1] 20 50  - snthresh: [1] 10  - prefilter: [1]   3 100  - mzCenterFun: [1] \"wMean\"  - integrate: [1] 1  - mzdiff: [1] -0.001  - fitgauss: [1] FALSE  - noise: [1] 0  - verboseColumns: [1] FALSE  - roiList: list()  - firstBaselineCheck: [1] TRUE  - roiScales: numeric(0)  - extendLengthMSW: [1] FALSE  - verboseBetaColumns: [1] FALSE #' Evaluate for Cystine cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt rtmin rtmax into intb maxo sn row column #' Evaluate for Methionine met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt rtmin rtmax into intb maxo sn row column #' Restrict the data to signal from cystine in the first sample cst <- lcms1[1L] |>   spectra() |>   filterRt(rt = c(208, 218)) |>   filterMzRange(mz = fData(eic_cystine)[\"cystine_13C_15N\", c(\"mzmin\", \"mzmax\")])  #' Show the number of peaks per m/z filtered spectra lengths(cst) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 #' Calculate the difference in m/z values between scans mz_diff <- cst |>     mz() |>     unlist() |>     diff() |>     abs()  #' Express differences in ppm range(mz_diff * 1e6 / mean(unlist(mz(cst)))) [1]  0.08829605 14.82188728 #' Parameters adapted for chromatographic peak detection on EICs. param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2,                        snthresh = 2)  #' Evaluate on the cystine ion cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt   rtmin   rtmax      into      intb      maxo sn row column  [1,] 209.251 207.577 212.878  4085.675  2911.376  2157.459  4   1      1  [2,] 209.251 206.182 213.995 24625.728 19074.407 12907.487  4   1      2  [3,] 209.252 207.020 214.274 19467.836 14594.041  9996.466  4   1      3  [4,] 209.251 207.577 212.041  4648.229  3202.617  2458.485  3   1      4  [5,] 208.974 206.184 213.159 23801.825 18126.978 11300.289  3   1      5  [6,] 209.250 207.018 213.714 25990.327 21036.768 13650.329  5   1      6  [7,] 209.252 207.857 212.879  4528.767  3259.039  2445.841  4   1      7  [8,] 209.252 207.299 213.995 23119.449 17274.140 12153.410  4   1      8  [9,] 208.972 206.740 212.878 28943.188 23436.119 14451.023  4   1      9 [10,] 209.252 207.578 213.437  4470.552  3065.402  2292.881  4   1     10 #' Evaluate on the methionine ion met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt   rtmin   rtmax     into     intb      maxo sn row column  [1,] 159.867 157.913 162.378 20026.61 14715.42 12555.601  4   1      1  [2,] 160.425 157.077 163.215 16827.76 11843.39  8407.699  3   1      2  [3,] 160.425 157.356 163.215 18262.45 12881.67  9283.375  3   1      3  [4,] 159.588 157.635 161.820 20987.72 15424.25 13327.811  4   1      4  [5,] 160.985 156.799 163.217 16601.72 11968.46 10012.396  4   1      5  [6,] 160.982 157.634 163.214 17243.24 12389.94  9150.079  4   1      6  [7,] 159.867 158.193 162.099 21120.10 16202.05 13531.844  3   1      7  [8,] 160.426 157.356 162.937 18937.40 13739.73 10336.000  3   1      8  [9,] 160.704 158.472 163.215 17882.21 12299.43  9395.548  3   1      9 [10,] 160.146 157.914 162.379 20275.80 14279.50 12669.821  3   1     10 #' Using the same settings, but with default snthresh param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms1 <- findChromPeaks(lcms1, param = param, chunkSize = 5)  #' Update EIC internal standard object eics_is_noprocess <- eic_is eic_is <- chromatogram(lcms1,,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) Processing chromatographic peaks fData(eic_is) <- fData(eics_is_noprocess) Processing chromatographic peaks #' set up the parameter param <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75)  #' Perform the peak refinement on the EICs eics <- refineChromPeaks(eics, param = param) plot(eics) #' Apply on whole dataset lcms1 <- refineChromPeaks(lcms1, param = param, chunkSize = 5) Reduced from 106714 to 89182 chromatographic peaks. chromPeakData(lcms1)$merged |>                       table() FALSE  TRUE 79908  9274 eics_is_chrompeaks <- eic_is  eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_chrompeaks)  eic_cystine <- eic_is[\"cystine_13C_15N\", ] eic_met <- eic_is[\"methionine_13C_15N\", ] #' Count the number of peaks per sample and summarize them in a table. data.frame(sample_name = sampleData(lcms1)$sample_name,            peak_count = as.integer(table(chromPeaks(lcms1)[, \"sample\"]))) |>     kable(format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"refine-identified-chromatographic-peaks","dir":"Articles","previous_headings":"Data preprocessing","what":"Refine identified chromatographic peaks","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"identification chromatographic peaks using CentWave algorithm can sometimes result artifacts, overlapping split peaks. address issue, refineChromPeaks() function utilized, conjunction MergeNeighboringPeaksParam, aims merging split peaks. show examples CentWave peak detection artifacts. examples pre-selected illustrate necessity next step:  Figure 13. Examples CentWave peak detection artifacts. cases signal presumably single type ion split two separate chromatographic peaks (indicated vertical line). MergeNeigboringPeaksParam allows combine split peaks. parameters algorithm defined : expandMz: Suggested kept relatively small (0.0015) prevent merging isotopes. expandRt: Usually set approximately half size average retention time width used chromatographic peak detection (case, 2.5 seconds). minProp: Used determine whether candidates merged. Chromatographic peaks overlapping m/z ranges (expanded side expandMz) tail--head distance retention time dimension less 2 * expandRt, signal higher minProp apex intensity chromatographic peak lower intensity, merged. Values parameter small avoid merging closely co-eluting ions, isomers. test settings EICs split peaks.  Figure 14. Examples CentWave peak detection artifacts merging. can observe artificially split peaks appropriately merged. Therefore, next apply settings entire dataset. peak merging, column \"merged\" result object’s chromPeakData() data frame can used evaluate chromatographic peaks result represent signal merged, originally identified chromatographic peaks. proceeding next preprocessing step generally suggested evaluate results chromatographic peak detection EICs e.g. internal standards compounds/ions known present samples. Additionally, evaluating comparing number identified chromatographic peaks samples data set can help spotting potentially problematic samples. count number chromatographic peaks per sample show numbers table. Table 5. Samples number identified chromatographic peaks. similar number chromatographic peaks identified within various samples data set. Additional options evaluate results chromatographic peak detection can implemented using plotChromPeaks() function summarizing results using base R commands.","code":"Processing chromatographic peaks #' set up the parameter param <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75)  #' Perform the peak refinement on the EICs eics <- refineChromPeaks(eics, param = param) plot(eics) #' Apply on whole dataset lcms1 <- refineChromPeaks(lcms1, param = param, chunkSize = 5) Reduced from 106714 to 89182 chromatographic peaks. chromPeakData(lcms1)$merged |>                       table() FALSE  TRUE 79908  9274 eics_is_chrompeaks <- eic_is  eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_chrompeaks)  eic_cystine <- eic_is[\"cystine_13C_15N\", ] eic_met <- eic_is[\"methionine_13C_15N\", ] #' Count the number of peaks per sample and summarize them in a table. data.frame(sample_name = sampleData(lcms1)$sample_name,            peak_count = as.integer(table(chromPeaks(lcms1)[, \"sample\"]))) |>     kable(format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"retention-time-alignment","dir":"Articles","previous_headings":"","what":"Retention time alignment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Despite using chromatographic settings conditions retention time shifts unavoidable. Indeed, performance instrument can change time, example due small variations environmental conditions, temperature pressure. shifts generally small samples measured within batch/measurement run, can considerable data experiment acquired across longer time period. evaluate presence shift extract plot BPC QC samples.  Figure 15. BPC QC samples. QC samples representing sample (pool) measured regular intervals measurement run experiment measured day. Still, small shifts can observed, especially region 100 150 seconds. facilitate proper correspondence signals across samples (hence definition LC-MS features), essential minimize differences retention times. Theoretically, proceed two steps: first select QC samples dataset first alignment , using -called anchor peaks. way can assume linear shift time, since always measuring sample different regular time intervals. Despite external QCs data set, still use subset-based alignment assuming retention time shifts independent different sample matrix (human serum plasma) instead mostly instrument-dependent. Note also possible manually specify anchor peaks, respectively retention times align data set external, reference, data set. information provided vignettes xcms package. calculating much adjust retention time samples, apply shift also study samples. xcms retention time alignment can performed using adjustRtime() function alignment algorithm. example use PeakGroups method (Smith et al. 2006) performs alignment minimizing differences retention times set anchor peaks different samples. method requires initial correspondence analysis match/group chromatographic peaks across samples algorithm selects anchor peaks alignment. initial correspondence, use PeakDensity approach (Smith et al. 2006) groups chromatographic peaks similar m/z retention time LC-MS features. parameters algorithm, can configured using PeakDensityParam object, sampleGroups, minFraction, binSize, ppm bw. binSize, ppm bw allow specify similar chromatographic peaks’ m/z retention time values need consider grouping feature. binSize ppm define required similarity m/z values. Within m/z bin (defined binSize ppm) areas along retention time axis high chromatographic peak density (considering peaks samples) identified, chromatographic peaks within regions considered grouping feature. High density areas identified using base R density() function, bw parameter: higher values define wider retention time areas, lower values require chromatographic peaks similar retention times. parameter can seen black line plot , corresponding smoothness density curve. Whether candidate peaks get grouped feature depends also parameters sampleGroups minFraction: sampleGroups provide, sample, sample group belongs . minFraction expected value 0 1 defining proportion samples within least one sample groups (defined sampleGroups) chromatographic peaks detected group feature. initial correspondence, parameters don’t need fully optimized. Selection dataset-specific parameter values described detail next section. dataset, use small values binSize ppm , importantly, also parameter bw, since data set ultra high performance (UHP) LC setup used. minFraction use high value (0.9) ensure features defined chromatographic peaks present almost samples one sample group (can used anchor peaks actual alignment). base alignment later QC samples hence define sampleGroups binary variable grouping samples either study, QC group.  Figure 16. Initial correspondence analysis. PeakGroups-based alignment can next performed using adjustRtime() function PeakGroupsParam parameter object. parameters algorithm : subsetAdjust subset: Allows subset alignment. base retention time alignment QC samples, .e., retention time shifts estimated based repeatedly measured samples. resulting adjustment applied entire data. data sets QC samples (e.g. sample pools) measured repeatedly, strongly suggest use method. Note also subset-based alignment samples ordered injection index (.e., order measured measurement run). minFraction: value 0 1 defining proportion samples (full data set, data subset defined subset) chromatographic peak identified use anchor peak. contrast PeakDensityParam parameter used define proportion within sample group. span: PeakGroups method allows, depending data, adjust regions along retention time axis differently. enable local alignments LOESS function used parameter defines degree smoothing function. Generally, values 0.4 0.6 used, however, suggested evaluate alignment results eventually adapt parameters result satisfactory. perform alignment data set based retention times anchor peaks defined subset QC samples. Alignment adjusted retention times spectra data set, well retention times identified chromatographic peaks. alignment performed, user evaluate results using plotAdjustedRtime() function. function visualizes difference adjusted raw retention time sample y-axis along adjusted retention time x-axis. Dot points represent position used anchor peak along retention time axis. optimal alignment areas along retention time axis, anchor peaks scattered retention time dimension.  Figure 17. Retention time alignment results. samples present data set measured within measurement run, resulting small retention time shifts. Therefore, little adjustments needed performed (shifts maximum 1 second can seen plot ). Generally, magnitude adjustment seen plots match expectation analyst. can also compare BPC alignment. get original data, .e. raw retention times, can use dropAdjustedRtime() function:  Figure 18. BPC alignment. largest shift can observed retention time range 120 130s. Apart retention time range, little changes can observed. next evaluate impact alignment EICs selected internal standards. thus first extract ion chromatograms alignment. can now evaluate alignment effect test ions. plot EICs alignment isotope labeled cystine methionine.  Figure 19. EICs cystine methionine alignment. non-endogenous cystine ion already well aligned difference minimal. methionine ion, however, shows improvement alignment. addition visual inspection results, also evaluate impact alignment comparing variance retention times internal standards alignment. end, first need identify chromatographic peaks sample m/z retention time close expected values internal standard. use matchValues() function MetaboAnnotation package (Rainer et al. 2022) using MzRtParam method identify chromatographic peaks similar m/z (+/- 50 ppm) retention time (+/- 10 seconds) internal standard’s values. parameters mzColname rtColname specify column names query () target (chromatographic peaks) contain m/z retention time values match entities. perform matching separately sample. internal standard every sample, use filterMatches() function SingleMatchParam() parameter select chromatographic peak highest intensity. now internal standard ID chromatographic peak sample likely represents signal ion. can now extract retention times chromatographic peaks alignment. can now evaluate impact alignment retention times internal standards across full data set:  Figure 20. Retention time variation internal standards alignment. average, variation retention times internal standards across samples slightly reduced alignment.","code":"#' Get QC samples QC_samples <- sampleData(lcms1)$phenotype == \"QC\"  #' extract BPC lcms1[QC_samples] |>     chromatogram(aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(col = col_phenotype[\"QC\"], main = \"BPC of QC samples\") |>     grid() # Initial correspondence analysis param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype == \"QC\",                           minFraction = 0.9,                           binSize = 0.01, ppm = 10,                           bw = 2) lcms1 <- groupChromPeaks(lcms1, param = param)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) #' Define parameters of choice subset <- which(sampleData(lcms1)$phenotype == \"QC\") param <- PeakGroupsParam(minFraction = 0.9, extraPeaks = 50, span = 0.5,                          subsetAdjust = \"average\",                          subset = subset)  #' Perform the alignment lcms1 <- adjustRtime(lcms1, param = param) #' Visualize alignment results plotAdjustedRtime(lcms1, col = paste0(col_sample, 80), peakGroupsPch = 1) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\") #' Get data before alignment lcms1_raw <- dropAdjustedRtime(lcms1)  #' Apply the adjusted retention time to our dataset lcms1 <- applyAdjustedRtime(lcms1) #' Plot the BPC before and after alignment par(mfrow = c(2, 1), mar = c(2, 1, 1, 0.5)) chromatogram(lcms1_raw, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC before alignment\", col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE)  chromatogram(lcms1, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC after alignment\",          col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE) #' Store the EICs before alignment eics_is_refined <- eic_is  #' Update the EICs eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_refined)  #' Extract the EICs for the test ions eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"] par(mfrow = c(2, 2), mar = c(4, 4.5, 2, 1))  old_eic_cystine <- eics_is_refined[\"cystine_13C_15N\"] plot(old_eic_cystine, main = \"Cystine before alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  old_eic_met <- eics_is_refined[\"methionine_13C_15N\"] plot(old_eic_met, main = \"Methionine before alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  plot(eic_cystine, main = \"Cystine after alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\")  plot(eic_met, main = \"Methionine after alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3) #' Extract the matrix with all chromatographic peaks and add a column #' with the ID of the chromatographic peak chrom_peaks <- chromPeaks(lcms1) |> as.data.frame() chrom_peaks$peak_id <- rownames(chrom_peaks)  #' Define the parameters for the matching and filtering of the matches p_1 <- MzRtParam(ppm = 50, toleranceRt = 10) p_2 <- SingleMatchParam(duplicates = \"top_ranked\", column = \"target_maxo\",                         decreasing = TRUE)  #' Iterate over samples and identify for each the chromatographic peaks #' with similar m/z and retention time than the onse from the internal #' standard, and extract among them the ID of the peaks with the #' highest intensity. intern_standard_peaks <- lapply(seq_along(lcms1), function(i) {     tmp <- chrom_peaks[chrom_peaks[, \"sample\"] == i, , drop = FALSE]     mtch <- matchValues(intern_standard, tmp,                         mzColname = c(\"mz\", \"mz\"),                         rtColname = c(\"RT\", \"rt\"),                         param = p_1)     mtch <- filterMatches(mtch, p_2)     mtch$target_peak_id }) |>     do.call(what = cbind) #' Define the index of the selected chromatographic peaks in the #' full chromPeaks matrix idx <- match(intern_standard_peaks, rownames(chromPeaks(lcms1)))  #' Extract the raw retention times for these rt_raw <- chromPeaks(lcms1_raw)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw))  #' Extract the adjusted retention times for these rt_adj <- chromPeaks(lcms1)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw)) list(all_raw = rowSds(rt_raw, na.rm = TRUE),      all_adj = rowSds(rt_adj, na.rm = TRUE)      ) |>     vioplot() grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"correspondence","dir":"Articles","previous_headings":"","what":"Correspondence","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"briefly touched subject correspondence determine anchor peaks alignment. Generally, goal correspondence analysis identify chromatographic peaks originate types ions samples experiment group LC-MS features. point, proper configuration parameter bw crucial. illustrate sensible choices parameter’s value can made. use plotChromPeakDensity() function simulate correspondence analysis default values PeakGroups extracted ion chromatograms two selected isotope labeled ions. plot shows EIC top panel, apex position chromatographic peaks different samples (y-axis), along retention time (x-axis) lower panel.  Figure 21. Initial correspondence analysis, Cystine.  Figure 22. Initial correspondence analysis, Methionine. Grouping peaks depends smoothness previousl mentionned density curve can configured parameter bw. seen , smoothness high properly group features. looking default parameters, can observe indeed, bw parameter set bw = 30, high modern UHPLC-MS setups. reduce value parameter 1.8 evaluate impact.  Figure 23. Correspondence analysis optimized parameters, Cystine.  Figure 24. Correspondence analysis optimized parameters, Methionine. can observe peaks now grouped accurately single feature test ion. important parameters optimized : binsize: data generated high resolution MS instrument, thus select low value paramete. ppm: TOF instruments, suggested use value ppm larger 0 accommodate higher measurement error instrument larger m/z values. minFraction: set minFraction = 0.75, hence defining features chromatographic peak identified least 75% samples one sample groups. sampleGroups: use information available sampleData’s \"phenotype\" column. correspondence analysis suggested evaluate results selected EICs. extract signal m/z similar isotope labeled methionine larger retention time range. Importantly, show actual correspondence results, set simulate = FALSE plotChromPeakDensity() function.  Figure 25. Correspondence analysis results, Methionine. hoped, signal two different ions now grouped separate features. Generally, correspondence results evaluated extracted chromatograms. Another interesting information look distribution features along retention time axis. results correspondence analysis now stored, along results preprocessing steps, within XcmsExperiment result object. correspondence results, .e., definition LC-MS features, can extracted using featureDefinitions() function. data frame provides average m/z retention time (columns \"mzmed\" \"rtmed\") characterize LC-MS feature. Column, \"peakidx\" contains indices chromatographic peaks assigned feature. actual abundances features, represent also final preprocessing results, can extracted featureValues() function: can note features (e.g. F0003 F0006) missing values samples. expected certain degree samples features, respectively ions, need present. address next section.","code":"#' Default parameter for the grouping and apply them to the test ions BPC param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 30)  param Object of class:  PeakDensityParam  Parameters:  - sampleGroups:  [1] \"QC\"  \"CVD\" \"CTR\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  - bw: [1] 30  - minFraction: [1] 0.5  - minSamples: [1] 1  - binSize: [1] 0.25  - maxFeatures: [1] 50  - ppm: [1] 0 plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Updating parameters param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 1.8)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Define the settings for the param param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype,                           minFraction = 0.75, binSize = 0.01, ppm = 10,                           bw = 1.8)  #' Apply to whole data lcms1 <- groupChromPeaks(lcms1, param = param) #' Extract chromatogram for an m/z similar to the one of the labeled methionine chr_test <- chromatogram(lcms1,                          mz = as.matrix(intern_standard[\"methionine_13C_15N\",                                                         c(\"mzmin\", \"mzmax\")]),                          rt = c(145, 200),                          aggregationFun = \"max\") plotChromPeakDensity(     chr_test, simulate = FALSE,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(chr_test)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(chr_test)[, \"sample\"]], 20),     peakPch = 16) # Bin features per RT slices vc <- featureDefinitions(lcms1)$rtmed breaks <- seq(0, max(vc, na.rm = TRUE) + 1, length.out = 15) |>     round(0) cuts <- cut(vc, breaks = breaks, include.lowest = TRUE)  table(cuts) cuts    [0,17]   (17,34]   (34,51]   (51,68]   (68,86]  (86,103] (103,120] (120,137]        15      2608       649        24       132        11        15       105 (137,154] (154,171] (171,188] (188,205] (205,222] (222,240]      1441      2202       410       869       554        33 #' Definition of the features featureDefinitions(lcms1) |>   head() mzmed    mzmin    mzmax    rtmed    rtmin    rtmax npeaks CTR CVD QC FT0001 50.98979 50.98949 50.99038 203.6001 203.1181 204.2331      8   1   3  4 FT0002 51.05904 51.05880 51.05941 191.1675 190.8787 191.5050      9   2   3  4 FT0003 51.98657 51.98631 51.98699 203.1467 202.6406 203.6710      7   0   3  4 FT0004 53.02036 53.02009 53.02043 203.2343 202.5652 204.0901     10   3   3  4 FT0005 53.52080 53.52051 53.52102 203.1936 202.8490 204.0901     10   3   3  4 FT0006 54.01007 54.00988 54.01015 159.2816 158.8499 159.4484      6   1   3  2             peakidx ms_level FT0001 7702, 16....        1 FT0002 7176, 16....        1 FT0003 7680, 17....        1 FT0004 7763, 17....        1 FT0005 8353, 17....        1 FT0006 5800, 15....        1 #' Extract feature abundances featureValues(lcms1, method = \"sum\") |>     head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422            NA              481.7436 FT0002              710.8078      875.9192            NA              693.6997 FT0003              445.5711      613.4410            NA              497.8866 FT0004            16994.5260    24605.7340     19766.707            17808.0933 FT0005             3284.2664     4526.0531      3521.822             3379.8909 FT0006            10681.7476    10009.6602            NA            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001            NA      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003            NA      634.9370              449.0933            NA FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006            NA     7296.4262                    NA     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785                    NA"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"gap-filling","dir":"Articles","previous_headings":"","what":"Gap filling","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"previously observed missing values (NA) attributed various reasons. Although might represent genuinely missing value, indicating ion (feature) truly present particular sample, also result failure preceding chromatographic peak detection step. crucial able recover missing values latter category much possible reduce eventual need data imputation. next examine prevalent missing values present dataset: can observe substantial number missing values values dataset. Let’s therefore delve process gap-filling. first evaluate example features chromatographic peak detected samples:  Figure 26. Examples chromatographic peaks missing values. instances, chromatographic peak identified one two selected samples (red line), hence missing value reported feature particular samples (blue line). However, cases, signal measured samples, thus, reporting missing value correct example. signal feature low, likely reason peak detection failed. rescue signal cases, fillChromPeaks() function can used ChromPeakAreaParam approach. method defines m/z-retention time area feature based detected peaks, signal respective ion expected. integrates intensities within area samples missing values feature. reported feature abundance. apply method using default parameters. fillChromPeaks() thus rescue missing data data set. Note , even sample ion present, worst case noise integrated, expected much lower actual chromatographic peak signal. Let’s look previously missing values :  Figure 27. Examples chromatographic peaks missing values gap-filling. gap-filling, also blue colored sample chromatographic peak present peak area reported feature abundance sample. assess effectiveness gap-filling method rescuing signals, can also plot average signal features least one missing value average filled-signal. advisable perform analysis repeatedly measured samples; case, QC samples used. , extract: Feature values detected chromatographic peaks setting filled = FALSE featuresValues() call. filled-signal first extracting detected gap-filled abundances replace values detected chromatographic peaks NA. , calculate row averages matrices plot .  detected (x-axis) gap-filled (y-axis) values QC samples highly correlated. Especially higher abundances, agreement high, low intensities, can expected, differences higher trending correlation line. , addition, fit linear regression line data summarize results linear regression line slope 1.12 intercept -1.62. indicates filled-signal average 1.12 times higher detected signal.","code":"#' Percentage of missing values sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 26.41597 ftidx <- which(is.na(rowSums(featureValues(lcms1)))) fts <- rownames(featureDefinitions(lcms1))[ftidx] farea <- featureArea(lcms1, features = fts[1:2])  chromatogram(lcms1[c(2, 3)],              rt = farea[, c(\"rtmin\", \"rtmax\")],              mz = farea[, c(\"mzmin\", \"mzmax\")]) |>     plot(col = c(\"red\", \"blue\"), lwd = 2) #' Fill in the missing values in the whole dataset lcms1 <- fillChromPeaks(lcms1, param = ChromPeakAreaParam(), chunkSize = 5)  #' Percentage of missing values after gap-filling sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 5.155492 #' Get only detected signal in QC samples vals_detect <- featureValues(lcms1, filled = FALSE)[, QC_samples]  #' Get detected and filled-in signal vals_filled <- featureValues(lcms1)[, QC_samples]  #' Replace detected signal with NA vals_filled[!is.na(vals_detect)] <- NA  #' Identify features with at least one filled peak has_filled <- is.na(rowSums(vals_detect))  #' Calculate row averages for features with missing values avg_detect <- rowMeans(vals_detect[has_filled, ], na.rm = TRUE) avg_filled <- rowMeans(vals_filled[has_filled, ], na.rm = TRUE)  #' Plot the values against each other (in log2 scale) plot(log2(avg_detect), log2(avg_filled),      xlim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      ylim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      pch = 21, bg = \"#00000020\", col = \"#00000080\") grid() abline(0, 1) #' fit a linear regression line to the data l <- lm(log2(avg_filled) ~ log2(avg_detect)) summary(l) Call: lm(formula = log2(avg_filled) ~ log2(avg_detect))  Residuals:     Min      1Q  Median      3Q     Max -6.8176 -0.3807  0.1725  0.5492  6.7504  Coefficients:                  Estimate Std. Error t value Pr(>|t|) (Intercept)      -1.62359    0.11545  -14.06   <2e-16 *** log2(avg_detect)  1.11763    0.01259   88.75   <2e-16 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  Residual standard error: 0.9366 on 2846 degrees of freedom   (846 observations deleted due to missingness) Multiple R-squared:  0.7346,    Adjusted R-squared:  0.7345 F-statistic:  7877 on 1 and 2846 DF,  p-value: < 2.2e-16"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"preprocessing-results","dir":"Articles","previous_headings":"","what":"Preprocessing results","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"final results LC-MS data preprocessing stored within XcmsExperiment object. includes identified chromatographic peaks, alignment results, well correspondence results. addition, guarantee reproducibility, result object keeps track performed processing steps, including individual parameter objects used configure . processHistory() function returns list various applied processing steps chronological order. , extract information first step performed preprocessing. processParam() function used extract actual parameter class used configure processing step. final result whole LC-MS data preprocessing two-dimensional matrix abundances -called LC-MS features samples. Note stage analysis features characterized m/z retention time don’t yet information metabolite feature represent. seen , feature matrix can extracted featureValues() function corresponding feature characteristics (.e., m/z retention time values) using featureDefinitions() function. Thus, two arrays extracted xcms result object used/imported analysis packages processing. example also exported tab delimited text files, used external tool, used, also MS2 spectra available, feature-based molecular networking GNPS analysis environment (Nothias et al. 2020). processing R, reference link raw MS data required, suggested extract xcms preprocessing result using quantify() function SummarizedExperiment object, Bioconductor’s default container data biological assays/experiments. simplifies integration Bioconductor analysis packages. quantify() function takes parameters featureValues() function, thus, call extract SummarizedExperiment detected, gap-filled, feature abundances: Sample identifications xcms result’s sampleData() now available colData() (column, sample annotations) featureDefinitions() rowData() (row, feature annotations). feature values added first assay() SummarizedExperiment even processing history available object’s metadata(). SummarizedExperiment supports multiple assays, numeric matrices dimensions. thus add detected gap-filled feature abundances additional assay SummarizedExperiment. Feature abundances can extracted assay() function. extract first 6 lines detected gap-filled feature abundances: advantage, addition container full preprocessing results also possibility easy intuitive creation data subsets ensuring data integrity. example easy subset full data selection features /samples: moving next step analysis, advisable save preprocessing results. multiple format options save , can found MsIO package documentation. save XcmsExperiment object file format handled alabster framework, ensures object can easily read languages like Python Javascript well loaded easily back R.","code":"#' Check first step of the process history processHistory(lcms1)[[1]] Object of class \"XProcessHistory\"  type: Peak detection  date: Wed Oct 30 16:17:12 2024  info:  fileIndex: 1,2,3,4,5,6,7,8,9,10  Parameter class: CentWaveParam  MS level(s) 1 #' Extract results as a SummarizedExperiment res <- quantify(lcms1, method = \"sum\", filled = FALSE) res class: SummarizedExperiment dim: 9068 10 metadata(6): '' '' ... '' '' assays(1): raw rownames(9068): FT0001 FT0002 ... FT9067 FT9068 rowData names(11): mzmed mzmin ... QC ms_level colnames(10): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML ... MS_F_POS.mzML   MS_QC_POOL_4_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index assays(res)$raw_filled <- featureValues(lcms1, method = \"sum\",                                         filled = TRUE )  #' Different assay in the SummarizedExperiment object assayNames(res) [1] \"raw\"        \"raw_filled\" assay(res, \"raw_filled\") |> head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422      411.3295              481.7436 FT0002              710.8078      875.9192      457.5920              693.6997 FT0003              445.5711      613.4410      277.5022              497.8866 FT0004            16994.5260    24605.7340    19766.7069            17808.0933 FT0005             3284.2664     4526.0531     3521.8221             3379.8909 FT0006            10681.7476    10009.6602     9599.9701            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001      314.7567      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003      425.3774      634.9370              449.0933      556.2544 FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006     4792.2390     7296.4262             2382.1788     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785             6911.5439 res[1:14, 3:8] class: SummarizedExperiment dim: 14 6 metadata(6): '' '' ... '' '' assays(2): raw raw_filled rownames(14): FT0001 FT0002 ... FT0013 FT0014 rowData names(11): mzmed mzmin ... QC ms_level colnames(6): MS_B_POS.mzML MS_QC_POOL_2_POS.mzML ...   MS_QC_POOL_3_POS.mzML MS_E_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index #' XcmsExperiment object:  saveMsObject(lcms1,              AlabasterParam(path = file.path(\"objects/preprocessed_lcms1\")))  #' Below we remove the processHistory of the res object to allow for export,  #' this need to be fixed later. metadata(res) <- list() saveObject(res, file.path(\"objects/preprocessed_res\"))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-normalization","dir":"Articles","previous_headings":"","what":"Data normalization","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"preprocessing, data normalization scaling might need applied remove technical variances data. simple approaches like median scaling can implemented lines R code, advanced normalization algorithms available packages Bioconductor’s preprocessCore. comprehensive workflow “Notame” also propose interesting normalization approach adaptable scalable user dataset (Klåvus et al. 2020). Generally, LC-MS data, bias can categorized three main groups(Broadhurst et al. 2018): Variances introduced sample collection initial processing, can include differences sample amounts. type bias expected sample-specific affect signals sample way. Methods like median scaling, LOESS quantiles normalization can adjust bias. Signal drifts along measurement samples experiment. Reasons drifts can related aging instrumentation used (columns, detector), also changes metabolite abundances characteristics due reactions modifications, oxidation. changes expected affect samples measured later run rather ones measured beginning. reason, bias can play major role large experiments bias can play major role large experiments measured long time range usually considered affect individual metabolites (metabolite groups) differently. adjustment, moving average linear regression-based approaches can used. latter can example performed using adjust_lm() function MetaboCoreUtils package. Batch-related biases. comprise noise specific larger set samples, can set samples measured one LC-MS measurement run (.e. one analysis plate) samples measured using specific batch reagents. noise assumed affect samples one batch way linear modeling-based approaches can used adjust . Unwanted variation can arise various sources highly dependent experiment. Therefore, data normalization chosen carefully based experimental design, statistical aims, balance accuracy precision achieved use auxiliary information. Sample preparation biases can evaluated using internal standards, depending however also added sample mixes sample processing. Repeated measurements QC samples hand allows estimate correct LC-MS specific biases. Also, proper planning experiment, measurement study samples random order, can largely avoid biases introduced mentioned sources variance. workflow present tools assess data quality evaluate need normalization well options normalization. space reasons able provide solutions adjust possible sources variation. principal component analysis (PCA) helpful tool initial, unsupervised, visualization data also provides insights potential quality issues data. order apply PCA measured feature abundances, need however impute (still present) missing values. assume missing values (gap-filling step) represent signal detection limit. cases, missing values can replaced random values sampled uniform distribution, ranging half smallest measured value smallest measured value specific feature. uniform distribution defined two parameters (minimum maximum) values equal probability selected. impute missing values approach add resulting data matrix new assay result object. PCA powerful tool detecting biases data. dimensionality reduction technique, enables visualization data lower-dimensional space. context LC-MS data, PCA can used identify overall biases batch, sample, injection index, etc. However, important note PCA linear method may able detect biases data. plotting PCA, apply log2 transform, center scale data. log2 transformation applied stabilize variance centering remove dependency absolute abundances.  Figure 28. PCA data coloured phenotypes. PCA shows clear separation study samples (plasma) QC samples (serum) first principal component (PC1). separation based phenotype visible third principal component (PC3). cases, can better option remove imputed values evaluate PCA . especially true imputed values replacing large proportion data. Global differences feature abundances samples (e.g. due sample-specific biases) can evaluated plotting distribution log2 transformed feature abundances using boxplots violin plots. show number detected chromatographic peaks per sample distribution log2 transformed feature abundances.  Figure 29. Number detected peaks feature abundances. upper part plot show gap filling steps allowed rescue substantial number NAs allowed us consistent number feature values per sample. consistency aligns asspumption every sample similar amount features detected. Additionally observe , average, signal distribution individual samples similar. alternative way evaluate differences abundances samples relative log abundance (RLA) plots (De Livera et al. 2012). RLA value abundance feature sample relative median abundance feature across multiple samples. can discriminate within group across group RLAs, depending whether abundance compared samples within sample group across samples. Within group RLA plots assess tightness replicates within groups median close zero low variation around . used across groups, allow compare behavior groups. Generally, -sample differences can easily spotted using RLA plots. calculate visualize within group RLA values using rowRla() function MsCoreUtils package defining parameter f sample groups.  Figure 30. RLA plot raw data filled data. RLA plot , can observe medians samples indeed centered around 0. Exception two CVD samples. Thus, distribution signals across samples comparable, differences seem present require sample normalization. previous RLA plot showed data biases need corrected. Therefore, implement -sample normalization using filled-features. process effectively mitigates variations influenced technical issues, differences sample preparation, processing injection methods. instance, employ commonly used technique known median scaling (De Livera et al. 2012). method involves computing median sample, followed determining median individual sample medians. ensures consistent median values sample throughout entire data set. Maintaining uniformity average total metabolite abundance across samples crucial effective implementation. process aims establish shared baseline central tendency metabolite abundance, mitigating impact sample-specific technical variations. approach fosters robust comparable analysis top features across data set. assumption normalizing based median, known lower sensitivity extreme values, enhances comparability top features ensures consistent average abundance across samples. median scaling calculated imputed non-imputed data, set stored separately within SummarizedExperiment object. approach facilitates testing various normalization strategies maintaining record processing steps undertaken, enabling easy regression previous stages necessary. crucial evaluate effectiveness normalization process. can achieved comparing distribution log2 transformed feature abundances normalization. Additionally, RLA plots can used assess tightness replicates within groups compare behavior groups.  Figure 31. PC1 PC2 data normalization. Normalization large impact PC1 PC2, separation study groups PC3 seems better difference QC samples lower normalization (see ).  Figure 32. PC3 PC4 data normalization. PCA plots show normalization process changed overall structure data. separation study QC samples remains . expected results normalization correct biological variance technical. compare RLA plots -sample normalization evaluate impact data.  Figure 33. RLA plot normalization. normalization process effectively centered data around median medians samples now closer zero. next evaluate coefficient variation (CV, also referred relative standard deviation RSD) features across samples either QC study samples. QC samples, CV represent technical noise, study samples include also expected biological differences. Thus, normalization reduce CV QC samples, slightly reducing CV study samples. CV calculated using rowRsd() function MetaboCoreUtils package. setting mad = TRUE use robust calculation using median absolute deviation instead standard deviation. Table 6. Distribution CV values across samples raw normalized data. table shows distribution CV raw normalized data. first column highlights % data given CV value, e.g. 25% data CV equal lower 0.04557 QC_raw data. anticipated, CV values QCs, reflect technical variance, lower compared study samples, include technical biological variance. Overall, minimal disparity exists raw normalized data, positive indication normalization process introduced bias dataset, also reflects little differences average abundances sample raw data. overall conclusion normalization process little variance present beginning, normalization however able center data around median (shown RLA plot). Given simplicity limited size example dataset, conclude normalization process stage. intricate datasets diverse biases, tailored approach devised. include also approaches adjust signal drifts batch effects. One possible option use linear-model based approach can example applied adjust_lm() function MetaboCoreUtils package.","code":"#' Load preprocessing results ## load(\"SumExp.RData\") ## loadResults(RDataParam(\"data.RData\"))  #' Impute missing values using an uniform distribution na_unidis <- function(z) {     na <- is.na(z)     if (any(na)) {         min = min(z, na.rm = TRUE)         z[na] <- runif(sum(na), min = min/2, max = min)     }     z }  #' Row-wise impute missing values and add the data as a new assay tmp <- apply(assay(res, \"raw_filled\"), MARGIN = 1, na_unidis) assays(res)$raw_filled_imputed <- t(tmp) #' Log2 transform and scale data vals <- assay(res, \"raw_filled_imputed\") |>     log2() |>     t() |>     scale(center = TRUE, scale = TRUE)  #' Perform the PCA pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  #' Plot the results vals_st <- cbind(vals, phenotype = res$phenotype) pca_12 <- autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) pca_34 <- autoplot(pca_res, data = vals_st, colour = 'phenotype',                    x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) grid.arrange(pca_12, pca_34, ncol = 1) layout(mat = matrix(1:3, ncol = 1), height = c(0.2, 0.2, 0.8))  par(mar = c(0.2, 4.5, 0.2, 3)) barplot(apply(assay(res, \"raw\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected peaks\", xaxt = \"n\", space = 0.012) grid(nx = NA, ny = NULL) barplot(apply(assay(res, \"raw_filled\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected + filled peaks\", xaxt = \"n\",         space = 0.012) grid(nx = NA, ny = NULL) vioplot(log2(assay(res, \"raw_filled\")), xaxt = \"n\",         ylab = expression(log[2]~feature~abundance),         col = paste0(col_sample, 80), border = col_sample) points(colMedians(log2(assay(res, \"raw_filled\")), na.rm = TRUE), type = \"b\",        pch = 1) grid(nx = NA, ny = NULL) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") par(mfrow = c(1, 1), mar = c(3.5, 4.5, 2.5, 1)) boxplot(MsCoreUtils::rowRla(assay(res, \"raw_filled\"),                             f = res$phenotype, transform = \"log2\"),         cex = 0.5, pch = 16,         col = paste0(col_sample, 80), ylab = \"RLA\",         border = col_sample, boxwex = 1,         outline = FALSE, xaxt = \"n\", main = \"Relative log abundance\",         cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = colData(res)$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty=3, lwd = 1, col = \"black\") legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") #' Compute median and generate normalization factor mdns <- apply(assay(res, \"raw_filled\"), MARGIN = 2,               median, na.rm = TRUE ) nf_mdn <- mdns / median(mdns)  #' divide dataset by median of median and create a new assay. assays(res)$norm <- sweep(assay(res, \"raw_filled\"), MARGIN = 2, nf_mdn, '/') assays(res)$norm_imputed <- sweep(assay(res, \"raw_filled_imputed\"), MARGIN = 2,                                   nf_mdn, '/') #' Data before normalization vals_st <- cbind(vals, phenotype = res$phenotype) pca_raw <- autoplot(pca_res, data = vals_st,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  #' Data after normalization vals_norm <- apply(assay(res, \"norm\"), MARGIN = 1, na_unidis) |>     log2() |>     scale(center = TRUE, scale = TRUE)  pca_res_norm <- prcomp(vals_norm, scale = FALSE, center = FALSE) vals_st_norm <- cbind(vals_norm, phenotype = res$phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) pca_raw <- autoplot(pca_res, data = vals_st ,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) par(mfrow = c(2, 1), mar = c(3.5, 4.5, 2.5, 1))  boxplot(rowRla(assay(res, \"raw_filled\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), cex.main = 1, outline = FALSE,         xaxt = \"n\", main = \"Raw data\", boxwex = 1) grid(nx = NA, ny = NULL) legend(\"topright\", inset = c(0, -0.2), col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE,        ncol = 3, cex = 0.7, bty = \"n\") abline(h = 0, lty=3, lwd = 1, col = \"black\")  boxplot(rowRla(assay(res, \"norm\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), boxwex = 1, outline = FALSE,         xaxt = \"n\", main = \"Normallized data\", cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = res$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty = 3, lwd = 1, col = \"black\") #' Calculate the CV values index_study <- res$phenotype %in% c(\"CTR\", \"CVD\") index_QC <- res$phenotype == \"QC\"  sample_res <- cbind(     QC_raw = rowRsd(assay(res, \"raw_filled\")[, index_QC],                     na.rm = TRUE, mad = TRUE),     QC_norm = rowRsd(assay(res, \"norm\")[, index_QC],                      na.rm = TRUE, mad = TRUE),     Study_raw = rowRsd(assay(res, \"raw_filled\")[, index_study],                        na.rm = TRUE, mad = TRUE),     Study_norm = rowRsd(assay(res, \"norm\")[, index_study],                         na.rm = TRUE, mad = TRUE) )  #' Summarize the values across features res_df <- data.frame(     QC_raw = quantile(sample_res[, \"QC_raw\"], na.rm = TRUE),     QC_norm = quantile(sample_res[, \"QC_norm\"], na.rm = TRUE),     Study_raw = quantile(sample_res[, \"Study_raw\"], na.rm = TRUE),     Study_norm = quantile(sample_res[, \"Study_norm\"], na.rm = TRUE) )  kable(res_df, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"initial-quality-assessment","dir":"Articles","previous_headings":"","what":"Initial quality assessment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"principal component analysis (PCA) helpful tool initial, unsupervised, visualization data also provides insights potential quality issues data. order apply PCA measured feature abundances, need however impute (still present) missing values. assume missing values (gap-filling step) represent signal detection limit. cases, missing values can replaced random values sampled uniform distribution, ranging half smallest measured value smallest measured value specific feature. uniform distribution defined two parameters (minimum maximum) values equal probability selected. impute missing values approach add resulting data matrix new assay result object. PCA powerful tool detecting biases data. dimensionality reduction technique, enables visualization data lower-dimensional space. context LC-MS data, PCA can used identify overall biases batch, sample, injection index, etc. However, important note PCA linear method may able detect biases data. plotting PCA, apply log2 transform, center scale data. log2 transformation applied stabilize variance centering remove dependency absolute abundances.  Figure 28. PCA data coloured phenotypes. PCA shows clear separation study samples (plasma) QC samples (serum) first principal component (PC1). separation based phenotype visible third principal component (PC3). cases, can better option remove imputed values evaluate PCA . especially true imputed values replacing large proportion data. Global differences feature abundances samples (e.g. due sample-specific biases) can evaluated plotting distribution log2 transformed feature abundances using boxplots violin plots. show number detected chromatographic peaks per sample distribution log2 transformed feature abundances.  Figure 29. Number detected peaks feature abundances. upper part plot show gap filling steps allowed rescue substantial number NAs allowed us consistent number feature values per sample. consistency aligns asspumption every sample similar amount features detected. Additionally observe , average, signal distribution individual samples similar. alternative way evaluate differences abundances samples relative log abundance (RLA) plots (De Livera et al. 2012). RLA value abundance feature sample relative median abundance feature across multiple samples. can discriminate within group across group RLAs, depending whether abundance compared samples within sample group across samples. Within group RLA plots assess tightness replicates within groups median close zero low variation around . used across groups, allow compare behavior groups. Generally, -sample differences can easily spotted using RLA plots. calculate visualize within group RLA values using rowRla() function MsCoreUtils package defining parameter f sample groups.  Figure 30. RLA plot raw data filled data. RLA plot , can observe medians samples indeed centered around 0. Exception two CVD samples. Thus, distribution signals across samples comparable, differences seem present require sample normalization.","code":"#' Load preprocessing results ## load(\"SumExp.RData\") ## loadResults(RDataParam(\"data.RData\"))  #' Impute missing values using an uniform distribution na_unidis <- function(z) {     na <- is.na(z)     if (any(na)) {         min = min(z, na.rm = TRUE)         z[na] <- runif(sum(na), min = min/2, max = min)     }     z }  #' Row-wise impute missing values and add the data as a new assay tmp <- apply(assay(res, \"raw_filled\"), MARGIN = 1, na_unidis) assays(res)$raw_filled_imputed <- t(tmp) #' Log2 transform and scale data vals <- assay(res, \"raw_filled_imputed\") |>     log2() |>     t() |>     scale(center = TRUE, scale = TRUE)  #' Perform the PCA pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  #' Plot the results vals_st <- cbind(vals, phenotype = res$phenotype) pca_12 <- autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) pca_34 <- autoplot(pca_res, data = vals_st, colour = 'phenotype',                    x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) grid.arrange(pca_12, pca_34, ncol = 1) layout(mat = matrix(1:3, ncol = 1), height = c(0.2, 0.2, 0.8))  par(mar = c(0.2, 4.5, 0.2, 3)) barplot(apply(assay(res, \"raw\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected peaks\", xaxt = \"n\", space = 0.012) grid(nx = NA, ny = NULL) barplot(apply(assay(res, \"raw_filled\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected + filled peaks\", xaxt = \"n\",         space = 0.012) grid(nx = NA, ny = NULL) vioplot(log2(assay(res, \"raw_filled\")), xaxt = \"n\",         ylab = expression(log[2]~feature~abundance),         col = paste0(col_sample, 80), border = col_sample) points(colMedians(log2(assay(res, \"raw_filled\")), na.rm = TRUE), type = \"b\",        pch = 1) grid(nx = NA, ny = NULL) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") par(mfrow = c(1, 1), mar = c(3.5, 4.5, 2.5, 1)) boxplot(MsCoreUtils::rowRla(assay(res, \"raw_filled\"),                             f = res$phenotype, transform = \"log2\"),         cex = 0.5, pch = 16,         col = paste0(col_sample, 80), ylab = \"RLA\",         border = col_sample, boxwex = 1,         outline = FALSE, xaxt = \"n\", main = \"Relative log abundance\",         cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = colData(res)$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty=3, lwd = 1, col = \"black\") legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"principal-component-analysis","dir":"Articles","previous_headings":"Data normalization","what":"Principal Component Analysis","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"PCA powerful tool detecting biases data. dimensionality reduction technique, enables visualization data lower-dimensional space. context LC-MS data, PCA can used identify overall biases batch, sample, injection index, etc. However, important note PCA linear method may able detect biases data. plotting PCA, apply log2 transform, center scale data. log2 transformation applied stabilize variance centering remove dependency absolute abundances.  Figure 28. PCA data coloured phenotypes. PCA shows clear separation study samples (plasma) QC samples (serum) first principal component (PC1). separation based phenotype visible third principal component (PC3). cases, can better option remove imputed values evaluate PCA . especially true imputed values replacing large proportion data.","code":"#' Log2 transform and scale data vals <- assay(res, \"raw_filled_imputed\") |>     log2() |>     t() |>     scale(center = TRUE, scale = TRUE)  #' Perform the PCA pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  #' Plot the results vals_st <- cbind(vals, phenotype = res$phenotype) pca_12 <- autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) pca_34 <- autoplot(pca_res, data = vals_st, colour = 'phenotype',                    x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) grid.arrange(pca_12, pca_34, ncol = 1)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"intensity-evaluation","dir":"Articles","previous_headings":"Data normalization","what":"Intensity evaluation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Global differences feature abundances samples (e.g. due sample-specific biases) can evaluated plotting distribution log2 transformed feature abundances using boxplots violin plots. show number detected chromatographic peaks per sample distribution log2 transformed feature abundances.  Figure 29. Number detected peaks feature abundances. upper part plot show gap filling steps allowed rescue substantial number NAs allowed us consistent number feature values per sample. consistency aligns asspumption every sample similar amount features detected. Additionally observe , average, signal distribution individual samples similar. alternative way evaluate differences abundances samples relative log abundance (RLA) plots (De Livera et al. 2012). RLA value abundance feature sample relative median abundance feature across multiple samples. can discriminate within group across group RLAs, depending whether abundance compared samples within sample group across samples. Within group RLA plots assess tightness replicates within groups median close zero low variation around . used across groups, allow compare behavior groups. Generally, -sample differences can easily spotted using RLA plots. calculate visualize within group RLA values using rowRla() function MsCoreUtils package defining parameter f sample groups.  Figure 30. RLA plot raw data filled data. RLA plot , can observe medians samples indeed centered around 0. Exception two CVD samples. Thus, distribution signals across samples comparable, differences seem present require sample normalization.","code":"layout(mat = matrix(1:3, ncol = 1), height = c(0.2, 0.2, 0.8))  par(mar = c(0.2, 4.5, 0.2, 3)) barplot(apply(assay(res, \"raw\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected peaks\", xaxt = \"n\", space = 0.012) grid(nx = NA, ny = NULL) barplot(apply(assay(res, \"raw_filled\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected + filled peaks\", xaxt = \"n\",         space = 0.012) grid(nx = NA, ny = NULL) vioplot(log2(assay(res, \"raw_filled\")), xaxt = \"n\",         ylab = expression(log[2]~feature~abundance),         col = paste0(col_sample, 80), border = col_sample) points(colMedians(log2(assay(res, \"raw_filled\")), na.rm = TRUE), type = \"b\",        pch = 1) grid(nx = NA, ny = NULL) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") par(mfrow = c(1, 1), mar = c(3.5, 4.5, 2.5, 1)) boxplot(MsCoreUtils::rowRla(assay(res, \"raw_filled\"),                             f = res$phenotype, transform = \"log2\"),         cex = 0.5, pch = 16,         col = paste0(col_sample, 80), ylab = \"RLA\",         border = col_sample, boxwex = 1,         outline = FALSE, xaxt = \"n\", main = \"Relative log abundance\",         cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = colData(res)$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty=3, lwd = 1, col = \"black\") legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"between-sample-normalisation","dir":"Articles","previous_headings":"","what":"Between sample normalisation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"previous RLA plot showed data biases need corrected. Therefore, implement -sample normalization using filled-features. process effectively mitigates variations influenced technical issues, differences sample preparation, processing injection methods. instance, employ commonly used technique known median scaling (De Livera et al. 2012). method involves computing median sample, followed determining median individual sample medians. ensures consistent median values sample throughout entire data set. Maintaining uniformity average total metabolite abundance across samples crucial effective implementation. process aims establish shared baseline central tendency metabolite abundance, mitigating impact sample-specific technical variations. approach fosters robust comparable analysis top features across data set. assumption normalizing based median, known lower sensitivity extreme values, enhances comparability top features ensures consistent average abundance across samples. median scaling calculated imputed non-imputed data, set stored separately within SummarizedExperiment object. approach facilitates testing various normalization strategies maintaining record processing steps undertaken, enabling easy regression previous stages necessary.","code":"#' Compute median and generate normalization factor mdns <- apply(assay(res, \"raw_filled\"), MARGIN = 2,               median, na.rm = TRUE ) nf_mdn <- mdns / median(mdns)  #' divide dataset by median of median and create a new assay. assays(res)$norm <- sweep(assay(res, \"raw_filled\"), MARGIN = 2, nf_mdn, '/') assays(res)$norm_imputed <- sweep(assay(res, \"raw_filled_imputed\"), MARGIN = 2,                                   nf_mdn, '/')"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"median-scaling","dir":"Articles","previous_headings":"Data normalization","what":"Median scaling","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"method involves computing median sample, followed determining median individual sample medians. ensures consistent median values sample throughout entire data set. Maintaining uniformity average total metabolite abundance across samples crucial effective implementation. process aims establish shared baseline central tendency metabolite abundance, mitigating impact sample-specific technical variations. approach fosters robust comparable analysis top features across data set. assumption normalizing based median, known lower sensitivity extreme values, enhances comparability top features ensures consistent average abundance across samples. median scaling calculated imputed non-imputed data, set stored separately within SummarizedExperiment object. approach facilitates testing various normalization strategies maintaining record processing steps undertaken, enabling easy regression previous stages necessary.","code":"#' Compute median and generate normalization factor mdns <- apply(assay(res, \"raw_filled\"), MARGIN = 2,               median, na.rm = TRUE ) nf_mdn <- mdns / median(mdns)  #' divide dataset by median of median and create a new assay. assays(res)$norm <- sweep(assay(res, \"raw_filled\"), MARGIN = 2, nf_mdn, '/') assays(res)$norm_imputed <- sweep(assay(res, \"raw_filled_imputed\"), MARGIN = 2,                                   nf_mdn, '/')"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"assessing-overall-effectiveness-of-the-normalization-approach","dir":"Articles","previous_headings":"","what":"Assessing overall effectiveness of the normalization approach","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"crucial evaluate effectiveness normalization process. can achieved comparing distribution log2 transformed feature abundances normalization. Additionally, RLA plots can used assess tightness replicates within groups compare behavior groups.  Figure 31. PC1 PC2 data normalization. Normalization large impact PC1 PC2, separation study groups PC3 seems better difference QC samples lower normalization (see ).  Figure 32. PC3 PC4 data normalization. PCA plots show normalization process changed overall structure data. separation study QC samples remains . expected results normalization correct biological variance technical. compare RLA plots -sample normalization evaluate impact data.  Figure 33. RLA plot normalization. normalization process effectively centered data around median medians samples now closer zero. next evaluate coefficient variation (CV, also referred relative standard deviation RSD) features across samples either QC study samples. QC samples, CV represent technical noise, study samples include also expected biological differences. Thus, normalization reduce CV QC samples, slightly reducing CV study samples. CV calculated using rowRsd() function MetaboCoreUtils package. setting mad = TRUE use robust calculation using median absolute deviation instead standard deviation. Table 6. Distribution CV values across samples raw normalized data. table shows distribution CV raw normalized data. first column highlights % data given CV value, e.g. 25% data CV equal lower 0.04557 QC_raw data. anticipated, CV values QCs, reflect technical variance, lower compared study samples, include technical biological variance. Overall, minimal disparity exists raw normalized data, positive indication normalization process introduced bias dataset, also reflects little differences average abundances sample raw data. overall conclusion normalization process little variance present beginning, normalization however able center data around median (shown RLA plot). Given simplicity limited size example dataset, conclude normalization process stage. intricate datasets diverse biases, tailored approach devised. include also approaches adjust signal drifts batch effects. One possible option use linear-model based approach can example applied adjust_lm() function MetaboCoreUtils package.","code":"#' Data before normalization vals_st <- cbind(vals, phenotype = res$phenotype) pca_raw <- autoplot(pca_res, data = vals_st,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  #' Data after normalization vals_norm <- apply(assay(res, \"norm\"), MARGIN = 1, na_unidis) |>     log2() |>     scale(center = TRUE, scale = TRUE)  pca_res_norm <- prcomp(vals_norm, scale = FALSE, center = FALSE) vals_st_norm <- cbind(vals_norm, phenotype = res$phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) pca_raw <- autoplot(pca_res, data = vals_st ,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) par(mfrow = c(2, 1), mar = c(3.5, 4.5, 2.5, 1))  boxplot(rowRla(assay(res, \"raw_filled\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), cex.main = 1, outline = FALSE,         xaxt = \"n\", main = \"Raw data\", boxwex = 1) grid(nx = NA, ny = NULL) legend(\"topright\", inset = c(0, -0.2), col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE,        ncol = 3, cex = 0.7, bty = \"n\") abline(h = 0, lty=3, lwd = 1, col = \"black\")  boxplot(rowRla(assay(res, \"norm\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), boxwex = 1, outline = FALSE,         xaxt = \"n\", main = \"Normallized data\", cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = res$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty = 3, lwd = 1, col = \"black\") #' Calculate the CV values index_study <- res$phenotype %in% c(\"CTR\", \"CVD\") index_QC <- res$phenotype == \"QC\"  sample_res <- cbind(     QC_raw = rowRsd(assay(res, \"raw_filled\")[, index_QC],                     na.rm = TRUE, mad = TRUE),     QC_norm = rowRsd(assay(res, \"norm\")[, index_QC],                      na.rm = TRUE, mad = TRUE),     Study_raw = rowRsd(assay(res, \"raw_filled\")[, index_study],                        na.rm = TRUE, mad = TRUE),     Study_norm = rowRsd(assay(res, \"norm\")[, index_study],                         na.rm = TRUE, mad = TRUE) )  #' Summarize the values across features res_df <- data.frame(     QC_raw = quantile(sample_res[, \"QC_raw\"], na.rm = TRUE),     QC_norm = quantile(sample_res[, \"QC_norm\"], na.rm = TRUE),     Study_raw = quantile(sample_res[, \"Study_raw\"], na.rm = TRUE),     Study_norm = quantile(sample_res[, \"Study_norm\"], na.rm = TRUE) )  kable(res_df, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"principal-component-analysis-1","dir":"Articles","previous_headings":"Data normalization","what":"Principal Component Analysis","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Figure 31. PC1 PC2 data normalization. Normalization large impact PC1 PC2, separation study groups PC3 seems better difference QC samples lower normalization (see ).  Figure 32. PC3 PC4 data normalization. PCA plots show normalization process changed overall structure data. separation study QC samples remains . expected results normalization correct biological variance technical.","code":"#' Data before normalization vals_st <- cbind(vals, phenotype = res$phenotype) pca_raw <- autoplot(pca_res, data = vals_st,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  #' Data after normalization vals_norm <- apply(assay(res, \"norm\"), MARGIN = 1, na_unidis) |>     log2() |>     scale(center = TRUE, scale = TRUE)  pca_res_norm <- prcomp(vals_norm, scale = FALSE, center = FALSE) vals_st_norm <- cbind(vals_norm, phenotype = res$phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) pca_raw <- autoplot(pca_res, data = vals_st ,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"intensity-evaluation-1","dir":"Articles","previous_headings":"Data normalization","what":"Intensity evaluation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"compare RLA plots -sample normalization evaluate impact data.  Figure 33. RLA plot normalization. normalization process effectively centered data around median medians samples now closer zero.","code":"par(mfrow = c(2, 1), mar = c(3.5, 4.5, 2.5, 1))  boxplot(rowRla(assay(res, \"raw_filled\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), cex.main = 1, outline = FALSE,         xaxt = \"n\", main = \"Raw data\", boxwex = 1) grid(nx = NA, ny = NULL) legend(\"topright\", inset = c(0, -0.2), col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE,        ncol = 3, cex = 0.7, bty = \"n\") abline(h = 0, lty=3, lwd = 1, col = \"black\")  boxplot(rowRla(assay(res, \"norm\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), boxwex = 1, outline = FALSE,         xaxt = \"n\", main = \"Normallized data\", cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = res$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty = 3, lwd = 1, col = \"black\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"coefficient-of-variation","dir":"Articles","previous_headings":"Data normalization","what":"Coefficient of variation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"next evaluate coefficient variation (CV, also referred relative standard deviation RSD) features across samples either QC study samples. QC samples, CV represent technical noise, study samples include also expected biological differences. Thus, normalization reduce CV QC samples, slightly reducing CV study samples. CV calculated using rowRsd() function MetaboCoreUtils package. setting mad = TRUE use robust calculation using median absolute deviation instead standard deviation. Table 6. Distribution CV values across samples raw normalized data. table shows distribution CV raw normalized data. first column highlights % data given CV value, e.g. 25% data CV equal lower 0.04557 QC_raw data. anticipated, CV values QCs, reflect technical variance, lower compared study samples, include technical biological variance. Overall, minimal disparity exists raw normalized data, positive indication normalization process introduced bias dataset, also reflects little differences average abundances sample raw data.","code":"#' Calculate the CV values index_study <- res$phenotype %in% c(\"CTR\", \"CVD\") index_QC <- res$phenotype == \"QC\"  sample_res <- cbind(     QC_raw = rowRsd(assay(res, \"raw_filled\")[, index_QC],                     na.rm = TRUE, mad = TRUE),     QC_norm = rowRsd(assay(res, \"norm\")[, index_QC],                      na.rm = TRUE, mad = TRUE),     Study_raw = rowRsd(assay(res, \"raw_filled\")[, index_study],                        na.rm = TRUE, mad = TRUE),     Study_norm = rowRsd(assay(res, \"norm\")[, index_study],                         na.rm = TRUE, mad = TRUE) )  #' Summarize the values across features res_df <- data.frame(     QC_raw = quantile(sample_res[, \"QC_raw\"], na.rm = TRUE),     QC_norm = quantile(sample_res[, \"QC_norm\"], na.rm = TRUE),     Study_raw = quantile(sample_res[, \"Study_raw\"], na.rm = TRUE),     Study_norm = quantile(sample_res[, \"Study_norm\"], na.rm = TRUE) )  kable(res_df, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"conclusion-on-normalization","dir":"Articles","previous_headings":"Data normalization","what":"Conclusion on normalization","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"overall conclusion normalization process little variance present beginning, normalization however able center data around median (shown RLA plot). Given simplicity limited size example dataset, conclude normalization process stage. intricate datasets diverse biases, tailored approach devised. include also approaches adjust signal drifts batch effects. One possible option use linear-model based approach can example applied adjust_lm() function MetaboCoreUtils package.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"quality-control-feature-prefiltering","dir":"Articles","previous_headings":"","what":"Quality control: Feature prefiltering","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"normalizing data can now pre-filter clean data performing statistical analysis. general, pre-filtering samples features performed remove outliers. copy original result object also keep unfiltered data later comparisons. eliminate features exhibit high variability dataset. Repeatedly measured QC samples typically serve robust basis cleansing datasets allowing identify features excessively high noise. data set external QC samples used, .e. pooled samples different collection using slightly different sample matrix, utility filtering somewhat limited. comprehensive description guidelines data filtering untargeted metabolomic studies, please refer (Broadhurst et al. 2018). first restrict data set features chromatographic peak detected least 2/3 samples least one study samples groups. ensures statistical tests carried later study samples performed reliable signal. Also, filter remove features mostly detected QC samples, study samples. filter can performed filterFeatures() function xcms package PercentMissingFilter setting. parameters filer: threshold: defines maximal acceptable percentage samples missing value(s) least one sample groups defined parameter f. f: factor defining sample groups. replacing \"QC\" sample group NA parameter f exclude QC samples evaluation consider study samples. threshold = 40 keep features peak detected 2 3 samples one sample groups. consider detected chromatographic peaks per sample, apply filter \"raw\" assay result object, contains abundance values detected chromatographic peaks (prior gap-filling). Following guidelines stated decided still use QC samples pre-filtering, basis represent similar bio-fluids study samples, thus, anticipate observing relatively similar metabolites affected similar measurement biases. therefore evaluate dispersion ratio (Dratio) (Broadhurst et al. 2018) features data set. accomplish task using function time DratioFilter parameter. filters exist function invite user explore decide best dataset. Dratio filter powerful tool identify features exhibit high variability data, relating variance observed QC samples study samples. setting threshold 0.4, remove features high degree variability QC study samples. example, feature deviation QC higher 40% (threshold = 0.4)deviation study samples removed. filtering step ensures features retained considerably lower technical biological variance. Note rowDratio() rowRsd() functions MetaboCoreUtils package used calculate actual numeric values estimates used filtering, e.g. evaluate distribution whole data set identify data set-dependent threshold values. Finally, evaluate number features left filtering steps calculate percentage features removed. dataset reduced 9068 4277 features. remove considerable amount features expected want focus reliable features analysis. rest analysis need separate QC samples study samples. store QC samples separate object later use. addition calculate CV QC samples add additional column rowData() result object. used later prioritize identified significant features e.g. low technical noise. Now data set preprocessed, normalized filtered, can start evaluate distribution data estimate variation due biology.","code":"#' Number of features before filtering nrow(res) [1] 9068 #' keep unfiltered object res_unfilt <- res #' Limit features to those with at least two detected peaks in one study group. #' Setting the value for QC samples to NA excludes QC samples from the #' calculation. f <- res$phenotype f[f == \"QC\"] <- NA f <- as.factor(f) res <- filterFeatures(res, PercentMissingFilter(f = f, threshold = 40),                       assay = \"raw\") 1808 features were removed #' Compute and filter based on the Dratio filter_dratio <- DratioFilter(threshold = 0.4,                               qcIndex = res$phenotype == \"QC\",                               studyIndex = res$phenotype != \"QC\",                               mad = TRUE) res <- filterFeatures(res, filter = filter_dratio, assay = \"norm_imputed\") 2983 features were removed #' Number of features after analysis nrow(res) [1] 4277 #' Percentage left: end/beginning nrow(res)/nrow(res_unfilt) * 100 [1] 47.16586 res_qc <- res[, res$phenotype == \"QC\"] res <- res[, res$phenotype != \"QC\"] #' Calculate the QC's CV and add as feature variable to the data set rowData(res)$qc_cv <- assay(res_qc, \"norm\") |>                rowRsd()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"differential-abundance-analysis","dir":"Articles","previous_headings":"","what":"Differential abundance analysis","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"normalization quality control, next step identify features differentially abundant study groups. crucial step allows us identify potential biomarkers metabolites associated study groups. various approaches methods available identification features interest. workflow use multiple linear regression analysis identify features significantly difference abundances CVD CTR study group. performing tests evaluate similarities study samples using PCA (excluding QC samples avoid influencing results).  Figure 34. PCA data normalization quality control. samples clearly separate study group PCA indicating differences metabolite profiles two groups. However, drives separation PC1 clear. evaluate whether explained available variable study, .e., age:  Figure 35. PCA colored age data normalization quality control. According PCA , PC1 seem related age. Even variance data set can’t explain stage, proceed (supervised) statistical tests identify features interest. compute linear models metabolite explaining observed feature abundance available study variables. also use base R function lm(), utilize R Biocpkg(\"limma\") package conduct differential abundance analysis: moderated test statistics (Smyth 2004) provided package specifically well suited experiments limited number replicates. tests use linear model ~ phenotype + age, hence explaining abundances one metabolite accounting study group assignment age individual. analysis might benefit inclusion study covariate associated PC2 explaining variance seen principal component, present analysis participant’s age disease association provided. define design study model.matrix() function fit feature-wise linear models log2-transformed abundances using lmFit() function. P-values significance association calculated using eBayes() function, also performs empirical Bayes-based robust estimation standard errors. See also excellent vignette/user guide limma package examples details linear model procedure. linear models fitted, can now proceed extract results. create data frame containing coefficients, raw adjusted p-values (applying Benjamini-Hochberg correction, .e., method = \"BH\" improved control false discovery rate), average intensity signals CVD CTR samples, indication whether feature deemed significant . consider metabolites adjusted p-value smaller 0.05 significant, also include (absolute) difference abundances cut-criteria. last, add differential abundance results result object’s rowData(). can now proceed visualize distribution raw adjusted p-values.  Figure 36. Distribution raw (left) adjusted p-values (right). histograms show distribution raw adjusted p-values. Except enrichment small p-values, raw p-values (less) uniformly distributed, indicates absence strong systematic biases data. adjusted p-values conservative account multiple testing; important fit linear model feature therefore perform large number tests leads high chance false positive findings. see features low p-values, indicating likely significantly different two study groups. plot adjusted p-values log2 fold change (average) abundances. volcano plot allow us visualize features significantly different two study groups. highlighted blue color plot .  Figure 37. Volcano plot showing analysis results. interesting features top corners volcano plot (.e., features large difference abundance groups small p-value). significant features negative coefficient (log2 fold change value) indicating abundance lower CVD samples compared CTR samples. features listed, along average difference (log2) abundance compared groups, adjusted p-values, average (log2) abundance sample group RSD (CV) QC samples table . Table 7. Features significant differences abundances. visualize EICs significant features evaluate (raw) signal. restrict MS data set study samples. Parameters keepFeatures = TRUE: ensures identified features retained `subset object. peakBg: defines (background) color individual chromatographic peak EIC object.  Figure 38. Extracted ion chromatograms significant features. EICs significant features show clear single peak. intensities (already observed ) much larger CTR CVD samples. exception second feature (second EIC top row), intensities significant features however generally low. might make challenging identify using LC-MS/MS setup.","code":"#' Define the colors for the plot col_sample <- col_phenotype[res$phenotype]  #' Log transform and scale the data for PCA analysis vals <- assay(res, \"norm_imputed\") |>     t() |>     log2() |>     scale(center = TRUE, scale = TRUE) pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  vals_st <- cbind(vals, phenotype = res$phenotype) autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) #' Add age to the PCA plot vals_st <- cbind(vals, age = res$age) autoplot(pca_res, data = vals_st , colour = 'age', scale = 0) #' Define the linear model to be applied to the data p.cut <- 0.05     # cut-off for significance. m.cut <- 0.5      # cut-off for log2 fold change  age <- res$age phenotype <- factor(res$phenotype) design <- model.matrix(~ phenotype + age)  #' Fit the linear model to the data, explaining metabolite #' concentrations by phenotype and age. fit <- lmFit(log2(assay(res, \"norm_imputed\")), design = design) fit <- eBayes(fit) #' Compile a result data frame tmp <- data.frame(     coef.CVD = fit$coefficients[, \"phenotypeCVD\"],     pvalue.CVD = fit$p.value[, \"phenotypeCVD\"],     adjp.CVD = p.adjust(fit$p.value[, \"phenotypeCVD\"], method = \"BH\"),     avg.CVD = rowMeans(         log2(assay(res, \"norm_imputed\")[, res$phenotype == \"CVD\"])),     avg.CTR = rowMeans(         log2(assay(res, \"norm_imputed\")[, res$phenotype == \"CTR\"])) ) tmp$significant.CVD <- tmp$adjp.CVD < 0.05 #' Add the results to the object's rowData rowData(res) <- cbind(rowData(res), tmp) #' Plot the distribution of p-values par(mfrow = c(1, 2)) hist(rowData(res)$pvalue.CVD, breaks = 64, xlab = \"p value\",      main = \"Distribution of raw p-values\",      cex.main = 1, cex.lab = 1, cex.axis = 1) hist(rowData(res)$adjp.CVD, breaks = 64, xlab = expression(p[BH]~value),      main = \"Distribution of adjusted p-values\",      cex.main = 1, cex.lab = 1, cex.axis = 1) #' Plot volcano plot of the statistical results par(mfrow = c(1, 1), mar = c(5, 5, 5, 1)) plot(rowData(res)$coef.CVD, -log10(rowData(res)$adjp.CVD),      xlab = expression(log[2]~difference),      ylab = expression(-log[10]~p[BH]), pch = 16, col = \"#00000060\",      cex.main = 1.5, cex.lab = 1.5, cex.axis = 1.3) grid() abline(h = -log10(0.05), col = \"#0000ffcc\") if (any(rowData(res)$significant.CVD)) {     points(rowData(res)$coef.CVD[rowData(res)$significant.CVD],            -log10(rowData(res)$adjp.CVD[rowData(res)$significant.CVD]),            col = \"#0000ffcc\") } # Table of significant features tab <- rowData(res)[rowData(res)$significant.CVD,                     c(\"mzmed\", \"rtmed\", \"coef.CVD\", \"adjp.CVD\",                       \"avg.CTR\", \"avg.CVD\", \"qc_cv\")] |>     as.data.frame() tab <- tab[order(abs(tab$coef.CVD), decreasing = TRUE), ] kable(tab, format = \"pipe\") #' Restrict the raw data to study samples. lcms1_study <- lcms1[sampleData(lcms1)$phenotype != \"QC\", keepFeatures = TRUE] #' Extract EICs for the significant features eic_sign <- featureChromatograms(     lcms1_study, features = rownames(tab), expandRt = 5, filled = TRUE)  #' Plot the EICs. plot(eic_sign, col = col_sample,      peakBg = paste0(col_sample[chromPeaks(eic_sign)[, \"sample\"]], 40)) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"annotation","dir":"Articles","previous_headings":"","what":"Annotation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"now identified features significant differences abundances two study groups. provide information metabolic pathways differentiate affected healthy individuals might hence also serve biomarkers. However, stage analysis know compounds/metabolites actually represent. thus need now annotate signals. Annotation can performed different level confidence Schymanski et al. (2014). lowest level annotation, highest rate false positive hits, bases features m/z ratios. Higher levels annotations employ fragment spectra (MS2 spectra) ions interest requiring however acquisition additional data. section, demonstrate multiple ways annotate significant features using functionality provided Bioconductor packages. Alternative approaches external software tools, may better suited, also discussed later section. data set acquired using LC-MS setup features thus characterized m/z retention times. retention time LC-setup-specific , without prior data/knowledge provide little information features’ identity. Modern MS instruments high accuracy m/z values therefore reliable estimates compound ion’s mass--charge ratio. first approach, use features’ m/z values match reference values, .e., exact masses chemical compounds provided reference database, case MassBank database. full MassBank data re-distributed Bioconductor’s AnnotationHub resource, simplifies integration reproducible R-based analysis workflows. load resource, list available MassBank data sets/releases load one . MassBank data provided self-contained SQLite database data can queried accessed CompoundDb Bioconductor package. use compounds() function extract small compound annotations database. MassBank (small compound annotation databases) provides (exact) molecular mass compound. Since almost small compounds neutral natural state, need first converted m/z values allow matching feature’s m/z. calculate m/z neutral mass, need assume ion (adduct) might generated measured metabolites employed electro-spray ionization. positive polarity, human serum samples, common ions protonated ([M+H]+), bear addition sodium ([M+Na]+) ammonium ([M+H-NH3]+) ions. match observed m/z values reference values potential ions use matchValues() function Mass2MzParam approach, allows specify types expected ions adducts parameter maximal allowed difference compared values using tolerance ppm parameters. first prepare data.frame significant features, set parameters matching perform comparison query features reference database. resulting Matched object shows 4 6 significant features matched ions compounds MassBank database. extract full result Matched object. Thus, total 43 ions compounds MassBank matched significant features based specified tolerance settings. Many compounds, different structure thus function/chemical property, identical chemical formula thus mass. Matching exclusively m/z features hence result many potentially false positive hits thus considered provide low confidence annotation. additional complication annotation resources, like MassBank, community maintained, contain large amount redundant information. reduce redundancy result table iterate hits feature keep matches unique compounds (identified INCHIKEY). INCHI INCHIKEY combine information compound’s chemical formula structure, different compounds can share chemical formula, different structure thus INCHI. Table 9. MS1 annotation results. table shows results MS1-based annotation process. can see four significant features matched. matches seem pretty accurate low ppm errors. deduplication performed considerably reduced number hits feature, first still matches ions large number compounds (chemical formula). Considering features’ m/z retention times MS1-based annotation increase annotation confidence, requires additional data, recording retention time thepure standard compound LC setup. alternative approach might provide better inside annotations help choose different annotations feature evaluate certain chemical properties possible matches. instance, LogP value, available several databases HMDB, provides insight given compound’s polarity. property highly affects interaction analyte column, usually also directly affects separation. Therefore, comparison analyte’s retention time polarity can help rule possible misidentifications. low confidence, MS1-based annotation can provide first candidate annotations confirmed rejected additional analyses. MS1 annotation fast efficient method annotate features therefore give first insight compounds significantly different two study groups. However, always accurate. MS2 data can provide higher level confidence annotation process provides, observed fragmentation pattern, information structure compound. MS2 data can generated LC-MS/MS measurement MS2 spectra recorded ions either data dependent acquisition (DDA) data independent acquisition (DIA) mode. Generally, advised include LC-MS/MS runs QC samples randomly selected study samples already acquisition MS1 data used quantification signals. alternative, addition, post-hoc LC-MS/MS acquisition can performed generate MS2 data needed annotation. present experiment, separate LC-MS/MS measurement conducted QC samples selected study samples generate data using inclusion list pre-selected ions. represent features found significantly different CVD CTR samples initial analysis full experiment. use subset second LC-MS/MS data set show data can used MS2-based annotation. differential abundance analysis found features significantly higher abundances CTR samples. Consequently, utilize MS2 data obtained CTR samples annotate significant features. load LC-MS/MS data experiment restrict data acquired CTR sample. Table 10. Samples LC-MS/MS data set. total 3 LC-MS/MS data files control samples, different collision energy fragment ions. show number MS1 MS2 spectra files. Compared number MS2 spectra, far less MS1 spectra acquired. configuration MS instrument set ensure ions specified inclusion list selected fragmentation, even intensity might low. setting, however, recorded MS2 spectra represent noise. plot shows location precursor ions m/z - retention time plane three files.  can see MS2 spectra recorded m/z interest along full retention time range, even actual ions eluting within certain retention time windows. next extract Spectra object MS data data object assign new spectra variable employed collision energy, extract data object sampleData. next filter MS data first restricting MS2 spectra removing mass peaks spectrum intensity lower 5% highest intensity spectrum, assuming low intensity peaks represent background signal. next remove also mass peaks m/z value greater equal precursor m/z ion. puts, later matching reference spectra, weight fragmentation pattern ions avoids hits based precursor m/z peak (hence similar mass compared compounds). last, restrict data spectra least two fragment peaks scale intensities sum 1 spectrum. similarity calculations affected scaling, makes visual comparison fragment spectra easier read. Finally, also speed later comparison spectra reference database, load full MS data memory (changing backend MsBackendMemory) apply processing steps performed data far. Keeping MS data memory performance benefits, generally suggested large data sets. evaluate impact present data set print addition size data object changing backend. thus moderate increase memory demand loading MS data memory (also filtered cleaned MS2 data). proceed match experimental MS2 spectra reference fragment spectra, workflow aim annotate features found significant differential abundance analysis. goal thus identify MS2 spectra second (LC-MS/MS) run represent fragments ions features data first (LC-MS) run. approach match MS2 spectra significant features determined earlier based precursor m/z retention time (given acceptable tolerance) feature’s m/z retention time. can easily done using featureArea() function effectively considers actual m/z retention time ranges features’ chromatographic peaks therefore increase chance finding correct match. however also assumes retention times first second run don’t differ much. Alternatively, need align retention times second LC-MS/MS data set first. first extract feature area, .e., m/z retention time ranges, significant features. next identify fragment spectra precursor m/z retention times within ranges. use filterRanges() function allows filter Spectra object using multiple ranges simultaneously. apply function separately feature (row matrix) extract MS2 spectra representing fragmentation information presumed feature’s ions. result apply() call list Spectra, element representing result one feature. exception last feature, multiple MS2 spectra identified. next combine list Spectra single Spectra object using concatenateSpectra() function add additional spectra variable containing respective feature identifier. now Spectra object fragment spectra significant features differential expression analysis. next build reference data need process way query spectra. extract fragment spectra MassBank database, restrict positive polarity data (since experiment acquired positive polarity) perform processing fragment spectra MassBank database. Note switch MsBackendMemory backend hence loading full data reference database memory. positive impact performance subsequent spectra matching, however also increase memory demand present analysis. Now Spectra object second run database spectra prepared, can proceed matching process. use matchSpectra() function MetaboAnnotation package CompareSpectraParam define settings matching. following parameters: requirePrecursor = TRUE: Limits spectra similarity calculations fragment spectra similar precursor m/z. tolerance ppm: Defines acceptable difference compared m/z values. relaxed tolerance settings ensure find matches even reference spectra acquired instruments lower accuracy. THRESHFUN: Defines matches report. , keep matches resulting spectra similarity score (calculated normalized dot product (Stein Scott 1994), default similarity function) larger 0.6. Thus, total 315 query MS2 spectra, 16 matched (least) one reference fragment spectrum. restrict results matching spectra extract metadata query target spectra well similarity score (complete list available metadata information can listed colnames() function). Now, query-target pairs spectra similarity higher 0.6. Similar MS1-based annotation also result table contains redundant information: multiple fragment spectra per feature also MassBank contains several fragment spectra compound, measured using differing collision energies MS instruments, different laboratories. thus iterate feature-compound pairs select one highest score. identifier compound, use fragment spectra’s INCHI-key, since compound names MassBank accepted consensus/controlled vocabularies. Table 9.MS2 annotation results. Thus, 6 significant features, one annotated compound based MS2-based approach. many reasons failure find matches features. Although MS2 spectra selected feature, appear represent noise, features, LC-MS/MS run, low MS1 signal recorded, indicating selected sample original compound might (longer) present. Also, reference databases contain predominantly fragment spectra protonated ([M+H]+) ions compounds, features might represent signal types ions result different fragmentation pattern. Finally, fragment spectra compounds interest might also simply present used reference database. Thus, combining information MS1- MS2 based annotation can annotate one feature considerable confidence. feature m/z 195.0879 retention time 32 seconds seems ion caffeine. result somewhat disappointing also clearly shows importance proper experimental planning need control potential confounding factors. present experiment, disease-specific biomarker identified, life-style property individuals suffering disease: coffee consumption probably contraindicated patients CVD group reduce risk heart arrhythmia. plot EIC feature highlighting retention time highest scoring MS2 spectra recorded create mirror plot comparing MS2 spectra reference fragment spectra caffeine.   plot clearly shows higher signal feature CTR compared CVD samples. QC samples exhibit lower highly consistent signal, suggesting absence strong technical noise biases raw data experiment. vertical line indicates retention time fragment spectrum best match reference spectrum. noted , since fragment spectra measured separate LC-MS/MS experiment, considered indication approximate retention time ions fragmented second experiment. fragment spectrum feature, shown upper panel right plot highly similar reference spectrum caffeine MassBank (shown lower panel). addition matching precursor m/z, two fragments (m/z intensity) present spectra. can also extract additional metadata matching reference spectrum, used collision energy, fragmentation mode, instrument type, instrument well ion (adduct) fragmented. present workflow highlights annotation performed within R using packages Bioconductor project, also excellent external softwares used alternative, SIRIUS (Dührkop et al. 2019), mummichog (Li et al. 2013) GNPS (Nothias et al. 2020) among others. use , data need exported format supported . MS2 spectra, data easily exported required MGF file format using MsBackendMgf Bioconductor package. Integration xcms feature-based molecular networking GNPS described GNPS documentation. alternative, addition, evidence potential matching chemical formula feature derived evaluating isotope pattern full MS1 scan. provide information isotope composition. Also , various functions isotopologues() MetaboCoreUtils package functionality envipat R package (Loos et al. 2015) used.","code":"#' load reference data ah <- AnnotationHub() #' List available MassBank data sets query(ah, \"MassBank\") AnnotationHub with 6 records # snapshotDate(): 2024-10-24 # $dataprovider: MassBank # $species: NA # $rdataclass: CompDb # additional mcols(): taxonomyid, genome, description, #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, #   rdatapath, sourceurl, sourcetype # retrieve records with, e.g., 'object[[\"AH107048\"]]'               title   AH107048 | MassBank CompDb for release 2021.03   AH107049 | MassBank CompDb for release 2022.06   AH111334 | MassBank CompDb for release 2022.12.1   AH116164 | MassBank CompDb for release 2023.06   AH116165 | MassBank CompDb for release 2023.09   AH116166 | MassBank CompDb for release 2023.11 #' Load one MAssBank release mb <- ah[[\"AH116166\"]] #' Extract compound annotations cmps <- compounds(mb, columns = c(\"name\", \"formula\",                                   \"exactmass\", \"inchikey\")) head(cmps) formula exactmass                    inchikey                 name 1    C27H29NO11  543.1741 AOJJSUZBOXZQNB-UHFFFAOYSA-N           Epirubicin 2      C40H54O4  598.4022 KFNGKYUGHHQDEE-AXWOCEAUSA-N Crassostreaxanthin A 3    C10H24N2O2  204.1838 AEUTYOVWOVBAKS-UWVGGRQHSA-N           Ethambutol 4     C16H27NO5  313.1889 LMFKRLGHEKVMNT-UJDVCPFMSA-N           Heliotrine 5 C20H15Cl3N2OS  435.9971 JLGKQTAYUIMGRK-UHFFFAOYSA-N        Sertaconazole 6      C15H14O5  274.0841 BWNCKEBBYADFPQ-UHFFFAOYSA-N    (R)Semivioxanthin #' Prepare query data frame rowData(res)$feature_id <- rownames(rowData(res)) res_sig <- res[rowData(res)$significant.CVD, ]  #' Setup parameters for the matching param <- Mass2MzParam(adducts = c(\"[M+H]+\", \"[M+Na]+\", \"[M+H-NH3]+\"),                       tolerance = 0, ppm = 5)  #' Perform the matching. mtch <- matchValues(res_sig, cmps, param = param, mzColname = \"mzmed\") mtch Object of class Matched Total number of matches: 43 Number of query objects: 6 (4 matched) Number of target objects: 25685 (43 matched) #' Extracting the results mtch_res <- matchedData(mtch, c(\"feature_id\", \"mzmed\", \"rtmed\",                                 \"adduct\", \"ppm_error\",                                 \"target_formula\", \"target_name\",                                 \"target_inchikey\")) mtch_res DataFrame with 45 rows and 8 columns         feature_id     mzmed     rtmed      adduct ppm_error target_formula        <character> <numeric> <numeric> <character> <numeric>    <character> FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 ...            ...       ...       ...         ...       ...            ... FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1867474      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.0614704      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2 FT1171      FT1171   229.130  181.0883     [M+Na]+ 3.0770838      C12H18N2O FT5606      FT5606   560.360   33.5492          NA        NA             NA          target_name target_inchikey          <character>     <character> FT0371 Benzohydro...   VDEUYMSGMP... FT0371 Trigonelli...   WWNNZCOKKK... FT0371 Salicylami...   SKZKKFZAGN... FT0371 4-Aminoben...   ALYNCZNDIQ... FT0371 Anthranili...   RWZYAGGXGH... ...              ...             ... FT0845      Caffeine   RYYVLZVUVI... FT0845      caffeine   RYYVLZVUVI... FT0845 1,3,7-TRIM...   RYYVLZVUVI... FT1171 Isoproturo...   PUIYMUZLKQ... FT5606            NA              NA rownames(mtch_res) <- NULL  #' Keep only info on features that machted - create a utility function for that mtch_res <- split(mtch_res, mtch_res$feature_id) |>     lapply(function(x) {         lapply(split(x, x$target_inchikey), function(z) {             z[which.min(z$ppm_error), ]         })     }) |>     unlist(recursive = FALSE) |>     do.call(what = rbind)  #' Display the results kable(mtch_res, format = \"pipe\") #' Load form the MetaboLights Database param <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                            assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                            \"hilic_metabolite_profiling.txt\"),                            filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                      param,                      keepOntology = FALSE,                      keepProtocol = FALSE,                      simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  # filter samples to keep MSMS data from CTR samples: sampleData(lcms2) <- sampleData(lcms2)[sampleData(lcms2)$phenotype == \"CTR\", ]  sampleData(lcms2) <- sampleData(lcms2)[grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ]  # Add fragmentation data information (from filenames) sampleData(lcms2)$fragmentation_mode <- c(\"CE20\", \"CE30\", \"CES\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, c(10, 240)) #' check the number of spectra per ms level spectra(lcms2) |>     msLevel() |>     split(spectraSampleIndex(lcms2)) |>     lapply(table) |>     do.call(what = cbind) 1    2    3    4   5    6    7    8   9   10   11   12 1 825  186  186  186 825  186  186  186 825  185  186  185 2 825 3121 3118 3124 825 3123 3118 3120 825 3117 3117 3116 plotPrecursorIons(lcms2) ms2_ctr <- spectra(lcms2) ms2_ctr$collision_energy <-     sampleData(lcms2)$fragmentation_mode[spectraSampleIndex(lcms2)] #' Remove low intensity peaks low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 }  ms2_ctr <- filterMsLevel(ms2_ctr, 2L) |>     filterIntensity(intensity = low_int) #' Remove precursor peaks and restrict to spectra with a minimum #' number of peaks ms2_ctr <- filterPrecursorPeaks(ms2_ctr, ppm = 50, mz = \">=\") ms2_ctr <- ms2_ctr[lengths(ms2_ctr) > 1] |>     scalePeaks() #' Size of the object before loading into memory print(object.size(ms2_ctr), units = \"MB\") 5.1 Mb #' Load the MS data subset into memory ms2_ctr <- setBackend(ms2_ctr, MsBackendMemory()) ms2_ctr <- applyProcessing(ms2_ctr)  #' Size of the object after loading into memory print(object.size(ms2_ctr), units = \"MB\") 18.2 Mb #' Define the m/z and retention time ranges for the significant features target <- featureArea(lcms1)[rownames(res_sig), ] target mzmin    mzmax     rtmin     rtmax FT0371 138.0544 138.0552 146.32270 152.86115 FT0565 161.0391 161.0407 159.00234 164.30799 FT0732 182.0726 182.0756  32.71242  42.28755 FT0845 195.0799 195.0887  30.73235  35.67337 FT1171 229.1282 229.1335 178.01450 183.35303 FT5606 560.3539 560.3656  32.06570  35.33456 #' Identify for each feature MS2 spectra with their precursor m/z and #' retention time within the feature's m/z and retention time range ms2_ctr_fts <- apply(target[, c(\"rtmin\", \"rtmax\", \"mzmin\", \"mzmax\")],                      MARGIN = 1, FUN = filterRanges, object = ms2_ctr,                      spectraVariables = c(\"rtime\", \"precursorMz\")) lengths(ms2_ctr_fts) FT0371 FT0565 FT0732 FT0845 FT1171 FT5606     38     36    135     68     38      0 l <- lengths(ms2_ctr_fts) #' Combine the individual Spectra objects ms2_ctr_fts <- concatenateSpectra(ms2_ctr_fts) #' Assign the feature identifier to each MS2 spectrum ms2_ctr_fts$feature_id <- rep(rownames(res_sig), l) ms2_ref <- Spectra(mb) |>     filterPolarity(1L) |>     filterIntensity(intensity = low_int) |>     filterPrecursorPeaks(ppm = 50, mz = \">=\") ms2_ref <- ms2_ref[lengths(ms2_ref) > 1] |>     scalePeaks() register(SerialParam()) #' Define the settings for the spectra matching. prm <- CompareSpectraParam(ppm = 40, tolerance = 0.05,                            requirePrecursor = TRUE,                            THRESHFUN = function(x) which(x >= 0.6))  ms2_mtch <- matchSpectra(ms2_ctr_fts, ms2_ref, param = prm) ms2_mtch Object of class MatchedSpectra Total number of matches: 214 Number of query objects: 315 (16 matched) Number of target objects: 69561 (21 matched) #' Keep only query spectra with matching reference spectra ms2_mtch <- ms2_mtch[whichQuery(ms2_mtch)]  #' Extract the results ms2_mtch_res <- matchedData(ms2_mtch) nrow(ms2_mtch_res) [1] 214 #' - split the result per feature #' - select for each feature the best matching result for each compound #' - combine the result again into a data frame ms2_mtch_res <-     ms2_mtch_res |>     split(f = paste(ms2_mtch_res$feature_id, ms2_mtch_res$target_inchikey)) |>     lapply(function(z) {         z[which.max(z$score), ]     }) |>     do.call(what = rbind) |>     as.data.frame()  #' List the best matching feature-compound pair pandoc.table(ms2_mtch_res[, c(\"feature_id\", \"target_name\", \"score\",                               \"target_inchikey\")],              style = \"rmarkdown\",              caption = \"Table 9.MS2 annotation results.\",              split.table = Inf) col_sample <- col_phenotype[sampleData(lcms1)$phenotype] #' Extract and plot EIC for the annotated feature eic <- featureChromatograms(lcms1, features = ms2_mtch_res$feature_id[1]) plot(eic, col = col_sample, peakCol = col_sample[chromPeaks(eic)[, \"sample\"]],      peakBg = paste0(col_sample[chromPeaks(eic)[, \"sample\"]], 20)) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1)  #' Identify the best matching query-target spectra pair idx <- which.max(ms2_mtch_res$score)  #' Indicate the retention time of the MS2 spectrum in the EIC plot abline(v = ms2_mtch_res$rtime[idx]) #' Get the index of the MS2 spectrum in the query object query_idx <- which(query(ms2_mtch)$.original_query_index ==                                   ms2_mtch_res$.original_query_index[idx]) query_ms2 <- query(ms2_mtch)[query_idx] #' Get the index of the MS2 spectrum in the target object target_idx <- which(target(ms2_mtch)$spectrum_id ==                                     ms2_mtch_res$target_spectrum_id[idx]) target_ms2 <- target(ms2_mtch)[target_idx]  #' Create a mirror plot comparing the two best matching spectra plotSpectraMirror(query_ms2, target_ms2) legend(\"topleft\",        legend = paste0(\"precursor m/z: \", format(precursorMz(query_ms2), 3))) spectraData(target_ms2, c(\"collisionEnergy_text\", \"fragmentation_mode\",                           \"instrument_type\", \"instrument\", \"adduct\")) |>     as.data.frame() collisionEnergy_text fragmentation_mode instrument_type 1         55 (nominal)                HCD     LC-ESI-ITFT                          instrument adduct 1 LTQ Orbitrap XL Thermo Scientific [M+H]+"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"ms1-based-annotation","dir":"Articles","previous_headings":"","what":"MS1-based annotation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"data set acquired using LC-MS setup features thus characterized m/z retention times. retention time LC-setup-specific , without prior data/knowledge provide little information features’ identity. Modern MS instruments high accuracy m/z values therefore reliable estimates compound ion’s mass--charge ratio. first approach, use features’ m/z values match reference values, .e., exact masses chemical compounds provided reference database, case MassBank database. full MassBank data re-distributed Bioconductor’s AnnotationHub resource, simplifies integration reproducible R-based analysis workflows. load resource, list available MassBank data sets/releases load one . MassBank data provided self-contained SQLite database data can queried accessed CompoundDb Bioconductor package. use compounds() function extract small compound annotations database. MassBank (small compound annotation databases) provides (exact) molecular mass compound. Since almost small compounds neutral natural state, need first converted m/z values allow matching feature’s m/z. calculate m/z neutral mass, need assume ion (adduct) might generated measured metabolites employed electro-spray ionization. positive polarity, human serum samples, common ions protonated ([M+H]+), bear addition sodium ([M+Na]+) ammonium ([M+H-NH3]+) ions. match observed m/z values reference values potential ions use matchValues() function Mass2MzParam approach, allows specify types expected ions adducts parameter maximal allowed difference compared values using tolerance ppm parameters. first prepare data.frame significant features, set parameters matching perform comparison query features reference database. resulting Matched object shows 4 6 significant features matched ions compounds MassBank database. extract full result Matched object. Thus, total 43 ions compounds MassBank matched significant features based specified tolerance settings. Many compounds, different structure thus function/chemical property, identical chemical formula thus mass. Matching exclusively m/z features hence result many potentially false positive hits thus considered provide low confidence annotation. additional complication annotation resources, like MassBank, community maintained, contain large amount redundant information. reduce redundancy result table iterate hits feature keep matches unique compounds (identified INCHIKEY). INCHI INCHIKEY combine information compound’s chemical formula structure, different compounds can share chemical formula, different structure thus INCHI. Table 9. MS1 annotation results. table shows results MS1-based annotation process. can see four significant features matched. matches seem pretty accurate low ppm errors. deduplication performed considerably reduced number hits feature, first still matches ions large number compounds (chemical formula). Considering features’ m/z retention times MS1-based annotation increase annotation confidence, requires additional data, recording retention time thepure standard compound LC setup. alternative approach might provide better inside annotations help choose different annotations feature evaluate certain chemical properties possible matches. instance, LogP value, available several databases HMDB, provides insight given compound’s polarity. property highly affects interaction analyte column, usually also directly affects separation. Therefore, comparison analyte’s retention time polarity can help rule possible misidentifications. low confidence, MS1-based annotation can provide first candidate annotations confirmed rejected additional analyses.","code":"#' load reference data ah <- AnnotationHub() #' List available MassBank data sets query(ah, \"MassBank\") AnnotationHub with 6 records # snapshotDate(): 2024-10-24 # $dataprovider: MassBank # $species: NA # $rdataclass: CompDb # additional mcols(): taxonomyid, genome, description, #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, #   rdatapath, sourceurl, sourcetype # retrieve records with, e.g., 'object[[\"AH107048\"]]'               title   AH107048 | MassBank CompDb for release 2021.03   AH107049 | MassBank CompDb for release 2022.06   AH111334 | MassBank CompDb for release 2022.12.1   AH116164 | MassBank CompDb for release 2023.06   AH116165 | MassBank CompDb for release 2023.09   AH116166 | MassBank CompDb for release 2023.11 #' Load one MAssBank release mb <- ah[[\"AH116166\"]] #' Extract compound annotations cmps <- compounds(mb, columns = c(\"name\", \"formula\",                                   \"exactmass\", \"inchikey\")) head(cmps) formula exactmass                    inchikey                 name 1    C27H29NO11  543.1741 AOJJSUZBOXZQNB-UHFFFAOYSA-N           Epirubicin 2      C40H54O4  598.4022 KFNGKYUGHHQDEE-AXWOCEAUSA-N Crassostreaxanthin A 3    C10H24N2O2  204.1838 AEUTYOVWOVBAKS-UWVGGRQHSA-N           Ethambutol 4     C16H27NO5  313.1889 LMFKRLGHEKVMNT-UJDVCPFMSA-N           Heliotrine 5 C20H15Cl3N2OS  435.9971 JLGKQTAYUIMGRK-UHFFFAOYSA-N        Sertaconazole 6      C15H14O5  274.0841 BWNCKEBBYADFPQ-UHFFFAOYSA-N    (R)Semivioxanthin #' Prepare query data frame rowData(res)$feature_id <- rownames(rowData(res)) res_sig <- res[rowData(res)$significant.CVD, ]  #' Setup parameters for the matching param <- Mass2MzParam(adducts = c(\"[M+H]+\", \"[M+Na]+\", \"[M+H-NH3]+\"),                       tolerance = 0, ppm = 5)  #' Perform the matching. mtch <- matchValues(res_sig, cmps, param = param, mzColname = \"mzmed\") mtch Object of class Matched Total number of matches: 43 Number of query objects: 6 (4 matched) Number of target objects: 25685 (43 matched) #' Extracting the results mtch_res <- matchedData(mtch, c(\"feature_id\", \"mzmed\", \"rtmed\",                                 \"adduct\", \"ppm_error\",                                 \"target_formula\", \"target_name\",                                 \"target_inchikey\")) mtch_res DataFrame with 45 rows and 8 columns         feature_id     mzmed     rtmed      adduct ppm_error target_formula        <character> <numeric> <numeric> <character> <numeric>    <character> FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 ...            ...       ...       ...         ...       ...            ... FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1867474      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.0614704      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2 FT1171      FT1171   229.130  181.0883     [M+Na]+ 3.0770838      C12H18N2O FT5606      FT5606   560.360   33.5492          NA        NA             NA          target_name target_inchikey          <character>     <character> FT0371 Benzohydro...   VDEUYMSGMP... FT0371 Trigonelli...   WWNNZCOKKK... FT0371 Salicylami...   SKZKKFZAGN... FT0371 4-Aminoben...   ALYNCZNDIQ... FT0371 Anthranili...   RWZYAGGXGH... ...              ...             ... FT0845      Caffeine   RYYVLZVUVI... FT0845      caffeine   RYYVLZVUVI... FT0845 1,3,7-TRIM...   RYYVLZVUVI... FT1171 Isoproturo...   PUIYMUZLKQ... FT5606            NA              NA rownames(mtch_res) <- NULL  #' Keep only info on features that machted - create a utility function for that mtch_res <- split(mtch_res, mtch_res$feature_id) |>     lapply(function(x) {         lapply(split(x, x$target_inchikey), function(z) {             z[which.min(z$ppm_error), ]         })     }) |>     unlist(recursive = FALSE) |>     do.call(what = rbind)  #' Display the results kable(mtch_res, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"ms2-based-annotation","dir":"Articles","previous_headings":"","what":"MS2-based annotation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"MS1 annotation fast efficient method annotate features therefore give first insight compounds significantly different two study groups. However, always accurate. MS2 data can provide higher level confidence annotation process provides, observed fragmentation pattern, information structure compound. MS2 data can generated LC-MS/MS measurement MS2 spectra recorded ions either data dependent acquisition (DDA) data independent acquisition (DIA) mode. Generally, advised include LC-MS/MS runs QC samples randomly selected study samples already acquisition MS1 data used quantification signals. alternative, addition, post-hoc LC-MS/MS acquisition can performed generate MS2 data needed annotation. present experiment, separate LC-MS/MS measurement conducted QC samples selected study samples generate data using inclusion list pre-selected ions. represent features found significantly different CVD CTR samples initial analysis full experiment. use subset second LC-MS/MS data set show data can used MS2-based annotation. differential abundance analysis found features significantly higher abundances CTR samples. Consequently, utilize MS2 data obtained CTR samples annotate significant features. load LC-MS/MS data experiment restrict data acquired CTR sample. Table 10. Samples LC-MS/MS data set. total 3 LC-MS/MS data files control samples, different collision energy fragment ions. show number MS1 MS2 spectra files. Compared number MS2 spectra, far less MS1 spectra acquired. configuration MS instrument set ensure ions specified inclusion list selected fragmentation, even intensity might low. setting, however, recorded MS2 spectra represent noise. plot shows location precursor ions m/z - retention time plane three files.  can see MS2 spectra recorded m/z interest along full retention time range, even actual ions eluting within certain retention time windows. next extract Spectra object MS data data object assign new spectra variable employed collision energy, extract data object sampleData. next filter MS data first restricting MS2 spectra removing mass peaks spectrum intensity lower 5% highest intensity spectrum, assuming low intensity peaks represent background signal. next remove also mass peaks m/z value greater equal precursor m/z ion. puts, later matching reference spectra, weight fragmentation pattern ions avoids hits based precursor m/z peak (hence similar mass compared compounds). last, restrict data spectra least two fragment peaks scale intensities sum 1 spectrum. similarity calculations affected scaling, makes visual comparison fragment spectra easier read. Finally, also speed later comparison spectra reference database, load full MS data memory (changing backend MsBackendMemory) apply processing steps performed data far. Keeping MS data memory performance benefits, generally suggested large data sets. evaluate impact present data set print addition size data object changing backend. thus moderate increase memory demand loading MS data memory (also filtered cleaned MS2 data). proceed match experimental MS2 spectra reference fragment spectra, workflow aim annotate features found significant differential abundance analysis. goal thus identify MS2 spectra second (LC-MS/MS) run represent fragments ions features data first (LC-MS) run. approach match MS2 spectra significant features determined earlier based precursor m/z retention time (given acceptable tolerance) feature’s m/z retention time. can easily done using featureArea() function effectively considers actual m/z retention time ranges features’ chromatographic peaks therefore increase chance finding correct match. however also assumes retention times first second run don’t differ much. Alternatively, need align retention times second LC-MS/MS data set first. first extract feature area, .e., m/z retention time ranges, significant features. next identify fragment spectra precursor m/z retention times within ranges. use filterRanges() function allows filter Spectra object using multiple ranges simultaneously. apply function separately feature (row matrix) extract MS2 spectra representing fragmentation information presumed feature’s ions. result apply() call list Spectra, element representing result one feature. exception last feature, multiple MS2 spectra identified. next combine list Spectra single Spectra object using concatenateSpectra() function add additional spectra variable containing respective feature identifier. now Spectra object fragment spectra significant features differential expression analysis. next build reference data need process way query spectra. extract fragment spectra MassBank database, restrict positive polarity data (since experiment acquired positive polarity) perform processing fragment spectra MassBank database. Note switch MsBackendMemory backend hence loading full data reference database memory. positive impact performance subsequent spectra matching, however also increase memory demand present analysis. Now Spectra object second run database spectra prepared, can proceed matching process. use matchSpectra() function MetaboAnnotation package CompareSpectraParam define settings matching. following parameters: requirePrecursor = TRUE: Limits spectra similarity calculations fragment spectra similar precursor m/z. tolerance ppm: Defines acceptable difference compared m/z values. relaxed tolerance settings ensure find matches even reference spectra acquired instruments lower accuracy. THRESHFUN: Defines matches report. , keep matches resulting spectra similarity score (calculated normalized dot product (Stein Scott 1994), default similarity function) larger 0.6. Thus, total 315 query MS2 spectra, 16 matched (least) one reference fragment spectrum. restrict results matching spectra extract metadata query target spectra well similarity score (complete list available metadata information can listed colnames() function). Now, query-target pairs spectra similarity higher 0.6. Similar MS1-based annotation also result table contains redundant information: multiple fragment spectra per feature also MassBank contains several fragment spectra compound, measured using differing collision energies MS instruments, different laboratories. thus iterate feature-compound pairs select one highest score. identifier compound, use fragment spectra’s INCHI-key, since compound names MassBank accepted consensus/controlled vocabularies. Table 9.MS2 annotation results. Thus, 6 significant features, one annotated compound based MS2-based approach. many reasons failure find matches features. Although MS2 spectra selected feature, appear represent noise, features, LC-MS/MS run, low MS1 signal recorded, indicating selected sample original compound might (longer) present. Also, reference databases contain predominantly fragment spectra protonated ([M+H]+) ions compounds, features might represent signal types ions result different fragmentation pattern. Finally, fragment spectra compounds interest might also simply present used reference database. Thus, combining information MS1- MS2 based annotation can annotate one feature considerable confidence. feature m/z 195.0879 retention time 32 seconds seems ion caffeine. result somewhat disappointing also clearly shows importance proper experimental planning need control potential confounding factors. present experiment, disease-specific biomarker identified, life-style property individuals suffering disease: coffee consumption probably contraindicated patients CVD group reduce risk heart arrhythmia. plot EIC feature highlighting retention time highest scoring MS2 spectra recorded create mirror plot comparing MS2 spectra reference fragment spectra caffeine.   plot clearly shows higher signal feature CTR compared CVD samples. QC samples exhibit lower highly consistent signal, suggesting absence strong technical noise biases raw data experiment. vertical line indicates retention time fragment spectrum best match reference spectrum. noted , since fragment spectra measured separate LC-MS/MS experiment, considered indication approximate retention time ions fragmented second experiment. fragment spectrum feature, shown upper panel right plot highly similar reference spectrum caffeine MassBank (shown lower panel). addition matching precursor m/z, two fragments (m/z intensity) present spectra. can also extract additional metadata matching reference spectrum, used collision energy, fragmentation mode, instrument type, instrument well ion (adduct) fragmented.","code":"#' Load form the MetaboLights Database param <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                            assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                            \"hilic_metabolite_profiling.txt\"),                            filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                      param,                      keepOntology = FALSE,                      keepProtocol = FALSE,                      simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  # filter samples to keep MSMS data from CTR samples: sampleData(lcms2) <- sampleData(lcms2)[sampleData(lcms2)$phenotype == \"CTR\", ]  sampleData(lcms2) <- sampleData(lcms2)[grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ]  # Add fragmentation data information (from filenames) sampleData(lcms2)$fragmentation_mode <- c(\"CE20\", \"CE30\", \"CES\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, c(10, 240)) #' check the number of spectra per ms level spectra(lcms2) |>     msLevel() |>     split(spectraSampleIndex(lcms2)) |>     lapply(table) |>     do.call(what = cbind) 1    2    3    4   5    6    7    8   9   10   11   12 1 825  186  186  186 825  186  186  186 825  185  186  185 2 825 3121 3118 3124 825 3123 3118 3120 825 3117 3117 3116 plotPrecursorIons(lcms2) ms2_ctr <- spectra(lcms2) ms2_ctr$collision_energy <-     sampleData(lcms2)$fragmentation_mode[spectraSampleIndex(lcms2)] #' Remove low intensity peaks low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 }  ms2_ctr <- filterMsLevel(ms2_ctr, 2L) |>     filterIntensity(intensity = low_int) #' Remove precursor peaks and restrict to spectra with a minimum #' number of peaks ms2_ctr <- filterPrecursorPeaks(ms2_ctr, ppm = 50, mz = \">=\") ms2_ctr <- ms2_ctr[lengths(ms2_ctr) > 1] |>     scalePeaks() #' Size of the object before loading into memory print(object.size(ms2_ctr), units = \"MB\") 5.1 Mb #' Load the MS data subset into memory ms2_ctr <- setBackend(ms2_ctr, MsBackendMemory()) ms2_ctr <- applyProcessing(ms2_ctr)  #' Size of the object after loading into memory print(object.size(ms2_ctr), units = \"MB\") 18.2 Mb #' Define the m/z and retention time ranges for the significant features target <- featureArea(lcms1)[rownames(res_sig), ] target mzmin    mzmax     rtmin     rtmax FT0371 138.0544 138.0552 146.32270 152.86115 FT0565 161.0391 161.0407 159.00234 164.30799 FT0732 182.0726 182.0756  32.71242  42.28755 FT0845 195.0799 195.0887  30.73235  35.67337 FT1171 229.1282 229.1335 178.01450 183.35303 FT5606 560.3539 560.3656  32.06570  35.33456 #' Identify for each feature MS2 spectra with their precursor m/z and #' retention time within the feature's m/z and retention time range ms2_ctr_fts <- apply(target[, c(\"rtmin\", \"rtmax\", \"mzmin\", \"mzmax\")],                      MARGIN = 1, FUN = filterRanges, object = ms2_ctr,                      spectraVariables = c(\"rtime\", \"precursorMz\")) lengths(ms2_ctr_fts) FT0371 FT0565 FT0732 FT0845 FT1171 FT5606     38     36    135     68     38      0 l <- lengths(ms2_ctr_fts) #' Combine the individual Spectra objects ms2_ctr_fts <- concatenateSpectra(ms2_ctr_fts) #' Assign the feature identifier to each MS2 spectrum ms2_ctr_fts$feature_id <- rep(rownames(res_sig), l) ms2_ref <- Spectra(mb) |>     filterPolarity(1L) |>     filterIntensity(intensity = low_int) |>     filterPrecursorPeaks(ppm = 50, mz = \">=\") ms2_ref <- ms2_ref[lengths(ms2_ref) > 1] |>     scalePeaks() register(SerialParam()) #' Define the settings for the spectra matching. prm <- CompareSpectraParam(ppm = 40, tolerance = 0.05,                            requirePrecursor = TRUE,                            THRESHFUN = function(x) which(x >= 0.6))  ms2_mtch <- matchSpectra(ms2_ctr_fts, ms2_ref, param = prm) ms2_mtch Object of class MatchedSpectra Total number of matches: 214 Number of query objects: 315 (16 matched) Number of target objects: 69561 (21 matched) #' Keep only query spectra with matching reference spectra ms2_mtch <- ms2_mtch[whichQuery(ms2_mtch)]  #' Extract the results ms2_mtch_res <- matchedData(ms2_mtch) nrow(ms2_mtch_res) [1] 214 #' - split the result per feature #' - select for each feature the best matching result for each compound #' - combine the result again into a data frame ms2_mtch_res <-     ms2_mtch_res |>     split(f = paste(ms2_mtch_res$feature_id, ms2_mtch_res$target_inchikey)) |>     lapply(function(z) {         z[which.max(z$score), ]     }) |>     do.call(what = rbind) |>     as.data.frame()  #' List the best matching feature-compound pair pandoc.table(ms2_mtch_res[, c(\"feature_id\", \"target_name\", \"score\",                               \"target_inchikey\")],              style = \"rmarkdown\",              caption = \"Table 9.MS2 annotation results.\",              split.table = Inf) col_sample <- col_phenotype[sampleData(lcms1)$phenotype] #' Extract and plot EIC for the annotated feature eic <- featureChromatograms(lcms1, features = ms2_mtch_res$feature_id[1]) plot(eic, col = col_sample, peakCol = col_sample[chromPeaks(eic)[, \"sample\"]],      peakBg = paste0(col_sample[chromPeaks(eic)[, \"sample\"]], 20)) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1)  #' Identify the best matching query-target spectra pair idx <- which.max(ms2_mtch_res$score)  #' Indicate the retention time of the MS2 spectrum in the EIC plot abline(v = ms2_mtch_res$rtime[idx]) #' Get the index of the MS2 spectrum in the query object query_idx <- which(query(ms2_mtch)$.original_query_index ==                                   ms2_mtch_res$.original_query_index[idx]) query_ms2 <- query(ms2_mtch)[query_idx] #' Get the index of the MS2 spectrum in the target object target_idx <- which(target(ms2_mtch)$spectrum_id ==                                     ms2_mtch_res$target_spectrum_id[idx]) target_ms2 <- target(ms2_mtch)[target_idx]  #' Create a mirror plot comparing the two best matching spectra plotSpectraMirror(query_ms2, target_ms2) legend(\"topleft\",        legend = paste0(\"precursor m/z: \", format(precursorMz(query_ms2), 3))) spectraData(target_ms2, c(\"collisionEnergy_text\", \"fragmentation_mode\",                           \"instrument_type\", \"instrument\", \"adduct\")) |>     as.data.frame() collisionEnergy_text fragmentation_mode instrument_type 1         55 (nominal)                HCD     LC-ESI-ITFT                          instrument adduct 1 LTQ Orbitrap XL Thermo Scientific [M+H]+"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"external-tools-or-alternative-annotation-approaches","dir":"Articles","previous_headings":"","what":"External tools or alternative annotation approaches","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"present workflow highlights annotation performed within R using packages Bioconductor project, also excellent external softwares used alternative, SIRIUS (Dührkop et al. 2019), mummichog (Li et al. 2013) GNPS (Nothias et al. 2020) among others. use , data need exported format supported . MS2 spectra, data easily exported required MGF file format using MsBackendMgf Bioconductor package. Integration xcms feature-based molecular networking GNPS described GNPS documentation. alternative, addition, evidence potential matching chemical formula feature derived evaluating isotope pattern full MS1 scan. provide information isotope composition. Also , various functions isotopologues() MetaboCoreUtils package functionality envipat R package (Loos et al. 2015) used.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"tutorial, describe end--end workflow LC-MS-based untargeted metabolomics experiments, conducted entirely within R using packages Bioconductor project base R functionality. excellent software exists perform similar analyses, power R-based workflow lies adaptability individual data sets research questions ability build reproducible workflows documentation. Due space restrictions don’t provide comprehensive listing methodologies individual analysis steps. advanced options approaches available, e.g., normalization data, however also heavily dependent size properties analyzed data set, well annotation features. result, found present analysis set features significant abundance differences compared groups. however reliably annotate single feature, related lifestyle individuals rather pathological properties investigated disease. low proportion annotated signals however uncommon untargeted metabolomics experiments reflects need comprehensive reliable reference annotation libraries.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"session-information","dir":"Articles","previous_headings":"","what":"Session information","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"","code":"sessionInfo() R version 4.4.1 (2024-06-14) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.5 LTS  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0  locale:  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C  [9] LC_ADDRESS=C               LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C  time zone: Etc/UTC tzcode source: system (glibc)  attached base packages: [1] stats4    stats     graphics  grDevices utils     datasets  methods [8] base  other attached packages:  [1] MetaboAnnotation_1.9.2       CompoundDb_1.9.5  [3] AnnotationFilter_1.29.0      AnnotationHub_3.13.3  [5] BiocFileCache_2.13.2         dbplyr_2.5.0  [7] gridExtra_2.3                ggfortify_0.4.17  [9] ggplot2_3.5.1                vioplot_0.5.0 [11] zoo_1.8-12                   sm_2.2-6.0 [13] pheatmap_1.0.12              RColorBrewer_1.1-3 [15] pander_0.6.5                 limma_3.61.12 [17] MetaboCoreUtils_1.13.0       xcms_4.5.0 [19] MsBackendMetaboLights_0.99.1 Spectra_1.15.13 [21] BiocParallel_1.39.0          alabaster.se_1.5.4 [23] alabaster.base_1.5.10        SummarizedExperiment_1.35.5 [25] Biobase_2.65.1               GenomicRanges_1.57.2 [27] GenomeInfoDb_1.41.2          IRanges_2.39.2 [29] S4Vectors_0.43.2             BiocGenerics_0.51.3 [31] MatrixGenerics_1.17.1        matrixStats_1.4.1 [33] MsIO_0.0.8                   MsExperiment_1.7.0 [35] ProtGenerics_1.37.1          readxl_1.4.3 [37] BiocStyle_2.33.1             quarto_1.4.4 [39] knitr_1.48  loaded via a namespace (and not attached):   [1] later_1.3.2                 bitops_1.0-9   [3] filelock_1.0.3              tibble_3.2.1   [5] cellranger_1.1.0            preprocessCore_1.67.1   [7] XML_3.99-0.17               lifecycle_1.0.4   [9] doParallel_1.0.17           processx_3.8.4  [11] lattice_0.22-6              MASS_7.3-61  [13] MultiAssayExperiment_1.31.5 magrittr_2.0.3  [15] rmarkdown_2.28              yaml_2.3.10  [17] MsCoreUtils_1.17.3          DBI_1.2.3  [19] abind_1.4-8                 zlibbioc_1.51.2  [21] purrr_1.0.2                 RCurl_1.98-1.16  [23] rappdirs_0.3.3              GenomeInfoDbData_1.2.13  [25] MSnbase_2.31.1              ncdf4_1.23  [27] codetools_0.2-20            DelayedArray_0.31.14  [29] xml2_1.3.6                  DT_0.33  [31] tidyselect_1.2.1            farver_2.1.2  [33] UCSC.utils_1.1.0            base64enc_0.1-3  [35] jsonlite_1.8.9              iterators_1.0.14  [37] foreach_1.5.2               tools_4.4.1  [39] progress_1.2.3              Rcpp_1.0.13  [41] glue_1.8.0                  SparseArray_1.5.45  [43] xfun_0.48                   dplyr_1.1.4  [45] HDF5Array_1.33.8            withr_3.0.2  [47] BiocManager_1.30.25         fastmap_1.2.0  [49] rhdf5filters_1.17.0         fansi_1.0.6  [51] digest_0.6.37               mime_0.12  [53] R6_2.5.1                    colorspace_2.1-1  [55] rsvg_2.6.1                  RSQLite_2.3.7  [57] utf8_1.2.4                  tidyr_1.3.1  [59] generics_0.1.3              prettyunits_1.2.0  [61] PSMatch_1.9.1               httr_1.4.7  [63] htmlwidgets_1.6.4           S4Arrays_1.5.11  [65] pkgconfig_2.0.3             gtable_0.3.6  [67] blob_1.2.4                  impute_1.79.0  [69] MassSpecWavelet_1.71.0      XVector_0.45.0  [71] htmltools_0.5.8.1           MALDIquant_1.22.3  [73] clue_0.3-65                 scales_1.3.0  [75] alabaster.matrix_1.5.10     png_0.1-8  [77] rstudioapi_0.17.1           reshape2_1.4.4  [79] rjson_0.2.23                curl_5.2.3  [81] cachem_1.1.0                rhdf5_2.49.0  [83] stringr_1.5.1               BiocVersion_3.20.0  [85] parallel_4.4.1              AnnotationDbi_1.67.0  [87] mzID_1.43.0                 vsn_3.73.0  [89] pillar_1.9.0                grid_4.4.1  [91] alabaster.schemas_1.5.0     vctrs_0.6.5  [93] MsFeatures_1.13.0           pcaMethods_1.97.0  [95] cluster_2.1.6               evaluate_1.0.1  [97] cli_3.6.3                   compiler_4.4.1  [99] rlang_1.1.4                 crayon_1.5.3 [101] labeling_0.4.3              QFeatures_1.15.3 [103] ChemmineR_3.57.1            ps_1.8.1 [105] affy_1.83.1                 plyr_1.8.9 [107] fs_1.6.4                    stringi_1.8.4 [109] munsell_0.5.1               Biostrings_2.73.2 [111] lazyeval_0.2.2              Matrix_1.7-1 [113] hms_1.1.3                   bit64_4.5.2 [115] Rhdf5lib_1.27.0             KEGGREST_1.45.1 [117] statmod_1.5.0               alabaster.ranges_1.5.2 [119] mzR_2.39.2                  igraph_2.1.1 [121] memoise_2.0.1               affyio_1.75.1 [123] bit_4.5.0"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"aknowledgment","dir":"Articles","previous_headings":"","what":"Aknowledgment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Thanks Steffen Neumann continuous work develop maintain xcms software…","code":""},{"path":[]},{"path":[]},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"certain experiments, aligning datasets recorded different times necessary. can involve comparing runs samples different laboratories matching MS2 data initial MS1 run. Variation retention time across laboratories LC systems often requires alignment step using adjustRtime() LamaParama parameter. described data description vignette, samples run twice: LC-MS mode LC-MS/MS mode. tutorial show align LC-MS/MS run preprocessed LC-MS dataset. following packages needed: Setting parallel processing improve efficiency process: First, let’s load pre-processed LC-MS object. xcms result object created End--end worflow vignette also available metabonaut R package. result object (XcmsExperiment object) stored using Bioconductor’s alabaster framework load object using readMsObject() function providing path stored data. import function also takes care eventually retrieving missing MS data files MetaboLights repository. Next, load unprocessed LC-MS/MS data MetaboLights database: adjust sampleData() LC-MS/MS object make easier access: Table 10. Samples LC-MS/MS data set. keep MS runs (MS/MS) remove pooled samples, focusing samples E common runs. alignment, ensure retention time (RT) ranges match datasets: need adjust RT range LC-MS/MS object match LC-MS data: evaluate retention time shifts, ’ll plot base peak chromatogram (BPC): Compare run1 sample run2 sample  Similarly, compare BPC sample E:  Perform peak detection refining alignment, detailed end--end vignette. setting applied. Now, attempt align two samples previous dataset. first step extract landmark features (referred lamas). achieve , identify features present every phenotype group lcms1 dataset. , categorize (using factor()) data phenotype retain QC samples. variable utilized filter features using PercentMissingFilter parameter within filterFeatures() function. , setting threshold = 0 select features present QC samples. lamas input look like alignment. terms method works, alignment algorithm matches chromatographic peaks experimental data lamas, fitting model based match adjust retention times minimize differences two datasets. Now can define parameter object LamaParama prepare alignment. Parameters tolerance, toleranceRt, ppm relate matching chromatographic peaks lamas. parameters related type fitting generated data points. details parameter overall method can found searching ?adjustRtime. example using default parameters. matchLamaChromPeaks() function facilitates assessment well lamas correspond chromatographic peaks file. extract matched results using matchedRtimes() function. used later evaluate alignment. Now can adjust retention time LC-MS/MS dataset using adjustRtime() function. extract base peak chromatogram (BPC) aligned object: evaluate performance alignment process, generate plots comparing alignment reference dataset (black) LC-MS data (red) (blue) adjustment.   Although overall matching imperfect due initial sample issues, certain regions show significant improvement. alignment signal’s start particularly well done. Specifically, regions right 150 seconds show substantial improvement. visualization distribution chromatographic peaks matched anchor peaks (lamas) Sample . red vertical lines represent positions matched peaks.  quantitatively assess quality alignment, compute distance chromatographic peaks LC-MS data anchor peaks (Lamas) alignment.  Furthermore, detailed examination matching model used fitting file possible. Numerical information can obtained using summarizeLamaMatch() function. , percentage chromatographic peaks utilized alignment can computed relative total number peaks file. Additionally, feasible directly plot() param object file interest, showcasing distribution chromatographic peaks along fitted model line.  tutorial demonstrated align LC-MS LC-MS/MS datasets correct retention time shifts, crucial handling data different runs platforms. preprocessed data, detected chromatographic peaks, used landmark features (lamas) QC samples adjust retention times via adjustRtime() function. Visual comparisons base peak chromatograms alignment, along distance calculations, showed clear improvements RT synchronization. Ultimately, aligning chromatographic data ensures subsequent analyses, feature extraction statistical comparisons, based consistent time points, improving data quality reliability. tutorial outlined end--end workflow can adapted various LC-MS-based metabolomics studies, helping researchers manage retention time variation effectively.","code":"library(MsIO) library(alabaster.se) library(MsBackendMetaboLights) library(xcms) library(MsExperiment) library(Spectra) library(vioplot) #' Set up parallel processing using 2 cores if (.Platform$OS.type == \"unix\") {     register(MulticoreParam(2)) } else {     register(SnowParam(2)) } lcms1 <- readMsObject(     XcmsExperiment(),     AlabasterParam(system.file(\"extdata\", \"preprocessed_lcms1\",                                package = \"metabonaut\"))) adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_1_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_A_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_B_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_2_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_C_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_D_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_3_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_E_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_F_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_4_POS.mzML' #' Load form the MetaboLights Database mlp <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                          assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                                             \"hilic_metabolite_profiling.txt\"),                          filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                       mlp,                       keepOntology = FALSE,                       keepProtocol = FALSE,                       simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") # Only keep MS run lcms2 <- lcms2[!grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ] range(rtime(lcms1)) [1]   9.674428 240.115311 range(rtime(lcms2)) [1]   0.275 480.176 #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, range(rtime(lcms1))) idx_A <- which(sampleData(lcms1)$sample_name == \"A\") idx_E <- which(sampleData(lcms1)$sample_name == \"E\") bpc1 <-chromatogram(lcms1[c(idx_A,idx_E)], aggregationFun = \"max\",                     msLevel = 1) Processing chromatographic peaks bpc2 <- chromatogram(lcms2, aggregationFun = \"max\", msLevel = 1) plot(bpc1[1, 1], col = \"#00000080\",      main = \"BPC sample A LC-MS vs A LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 1]), intensity(bpc2[1, 1]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") plot(bpc1[1, 2], col = \"#00000080\",      main = \"BPC sample E LC-MS vs E LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") cwp <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms2 <- findChromPeaks(lcms2, param = cwp, chunkSize = 2) mnpp <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75) lcms2 <- refineChromPeaks(lcms2, param = mnpp, chunkSize = 2) f <- sampleData(lcms1)$phenotype f[f != \"QC\"] <- NA lcms1 <- filterFeatures(lcms1, PercentMissingFilter(threshold = 0, f = f),                         filled = FALSE) 3694 features were removed lcms1_mz_rt <- featureDefinitions(lcms1)[, c(\"mzmed\",\"rtmed\")] head(lcms1_mz_rt) mzmed    rtmed FT0001 50.98979 203.6001 FT0002 51.05904 191.1675 FT0003 51.98657 203.1467 FT0004 53.02036 203.2343 FT0005 53.52080 203.1936 FT0007 54.01010 235.9032 nrow(lcms1_mz_rt) [1] 5374 lp <- LamaParama(lamas = lcms1_mz_rt, method = \"loess\", span = 0.5,                  outlierTolerance = 3, zeroWeight = 10, ppm =20,                  tolerance = 0, toleranceRt = 20, bs = \"tp\") lp <- matchLamasChromPeaks(lcms2, param = lp) ref_vs_obs <- matchedRtimes(lp) #' input into `adjustRtime()` lcms2 <- adjustRtime(lcms2, param = lp) lcms2 <- applyAdjustedRtime(lcms2) #' evaluate the results with BPC bpc2_adj <- chromatogram(lcms2, aggregationFun = \"max\",                          msLevel = 1) #' BPC of sample A par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1,1]), intensity(bpc2[1,1]),        type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 1], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1,1]), intensity(bpc2_adj[1,1]),        type = \"l\",        col = \"#0000ff80\") #' BPC of sample B par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 2], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 2], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1, 2]), intensity(bpc2_adj[1, 2]), type = \"l\",        col = \"#0000ff80\") #' BPC of the first sample with matches to lamas overlay par(mfrow = c(1, 1)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Distribution CP matched to Lamas\",      lwd = 1.5,      peakType = \"none\") points(rtime(bpc2_adj[1, 1]), intensity(bpc2_adj[1, 1]), type = \"l\",        col = \"#0000ff80\") grid() abline(v = ref_vs_obs[[1]]$obs, col = \"#c4114510\") # Extract data for sample 3 directly ref_obs_sample_1 <- ref_vs_obs[[\"1\"]]  # Calculate distances before and after alignment dist_before <- abs(ref_obs_sample_1$obs - ref_obs_sample_1$ref) dist_after <- abs(chromPeaks(lcms2)[ref_obs_sample_1$chromPeaksId,                                              \"rt\"] - ref_obs_sample_1$ref)  # Create a data frame for plotting distances <- data.frame(   Distance = c(dist_before, dist_after),   Alignment = rep(c(\"Before\", \"After\"), each = length(dist_before)) )  # Set factor levels for Alignment to ensure correct order distances$Alignment <- factor(distances$Alignment, levels = c(\"Before\", \"After\"))  # Plot distances between anchor peaks between the two runs before and after alignment. vioplot(Distance ~ Alignment, data = distances, xlab = \"\",         rectCol = \"#c4114580\",         lineCol = \"white\",         col=\"#17138fe8\",         border = \"white\",         ylab = \"Distance (s)\",         main = \"Distance to Anchor Peaks: Before vs. After Alignment\") #' Access summary of matches and model information summary <- summarizeLamaMatch(lp) summary Total_peaks Matched_peaks Total_lamas Model_summary 1        6832          1825        5374  1666, c(.... 2        6860          1785        5374  1617, c(.... 3        7588          2082        5374  1869, c(.... #' Coverage for each file summary$Matched_peaks / summary$Total_peaks * 100 [1] 26.71253 26.02041 27.43806 #' Access the information on the model of for the first file summary$Model_summary[[1]] Call: loess(formula = ref ~ obs, data = rt_map, weights = weights,     span = span)  Number of Observations: 1666 Equivalent Number of Parameters: 7.38 Residual Standard Error: 2.315 Trace of smoother matrix: 8.13  (exact)  Control settings:   span     :  0.5   degree   :  2   family   :  gaussian   surface  :  interpolate     cell = 0.2   normalize:  TRUE  parametric:  FALSE drop.square:  FALSE #' Plot obs vs. lcms1 with fitting line plot(lp, index = 1L, main = \"ChromPeaks versus Lamas for sample A\",      colPoint = \"red\") abline(0, 1, lty = 3, col = \"grey\") grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"load-preprocessed-lc-ms-object","dir":"Articles","previous_headings":"","what":"Load preprocessed LC-MS object","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"First, let’s load pre-processed LC-MS object. xcms result object created End--end worflow vignette also available metabonaut R package. result object (XcmsExperiment object) stored using Bioconductor’s alabaster framework load object using readMsObject() function providing path stored data. import function also takes care eventually retrieving missing MS data files MetaboLights repository.","code":"lcms1 <- readMsObject(     XcmsExperiment(),     AlabasterParam(system.file(\"extdata\", \"preprocessed_lcms1\",                                package = \"metabonaut\"))) adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_1_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_A_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_B_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_2_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_C_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_D_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_3_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_E_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_F_POS.mzML' adding rname 'ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS8735/FILES/MS_QC_POOL_4_POS.mzML'"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"load-unprocessed-lc-msms-data","dir":"Articles","previous_headings":"","what":"Load unprocessed LC-MS/MS data","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"Next, load unprocessed LC-MS/MS data MetaboLights database: adjust sampleData() LC-MS/MS object make easier access: Table 10. Samples LC-MS/MS data set. keep MS runs (MS/MS) remove pooled samples, focusing samples E common runs. alignment, ensure retention time (RT) ranges match datasets: need adjust RT range LC-MS/MS object match LC-MS data:","code":"#' Load form the MetaboLights Database mlp <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                          assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                                             \"hilic_metabolite_profiling.txt\"),                          filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                       mlp,                       keepOntology = FALSE,                       keepProtocol = FALSE,                       simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") # Only keep MS run lcms2 <- lcms2[!grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ] range(rtime(lcms1)) [1]   9.674428 240.115311 range(rtime(lcms2)) [1]   0.275 480.176 #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, range(rtime(lcms1)))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"comparing-chromatograms","dir":"Articles","previous_headings":"","what":"Comparing chromatograms","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"evaluate retention time shifts, ’ll plot base peak chromatogram (BPC): Compare run1 sample run2 sample  Similarly, compare BPC sample E:","code":"idx_A <- which(sampleData(lcms1)$sample_name == \"A\") idx_E <- which(sampleData(lcms1)$sample_name == \"E\") bpc1 <-chromatogram(lcms1[c(idx_A,idx_E)], aggregationFun = \"max\",                     msLevel = 1) Processing chromatographic peaks bpc2 <- chromatogram(lcms2, aggregationFun = \"max\", msLevel = 1) plot(bpc1[1, 1], col = \"#00000080\",      main = \"BPC sample A LC-MS vs A LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 1]), intensity(bpc2[1, 1]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") plot(bpc1[1, 2], col = \"#00000080\",      main = \"BPC sample E LC-MS vs E LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"peak-detection","dir":"Articles","previous_headings":"","what":"Peak detection","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"Perform peak detection refining alignment, detailed end--end vignette. setting applied.","code":"cwp <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms2 <- findChromPeaks(lcms2, param = cwp, chunkSize = 2) mnpp <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75) lcms2 <- refineChromPeaks(lcms2, param = mnpp, chunkSize = 2)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"alignment","dir":"Articles","previous_headings":"","what":"Alignment","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"Now, attempt align two samples previous dataset. first step extract landmark features (referred lamas). achieve , identify features present every phenotype group lcms1 dataset. , categorize (using factor()) data phenotype retain QC samples. variable utilized filter features using PercentMissingFilter parameter within filterFeatures() function. , setting threshold = 0 select features present QC samples. lamas input look like alignment. terms method works, alignment algorithm matches chromatographic peaks experimental data lamas, fitting model based match adjust retention times minimize differences two datasets. Now can define parameter object LamaParama prepare alignment. Parameters tolerance, toleranceRt, ppm relate matching chromatographic peaks lamas. parameters related type fitting generated data points. details parameter overall method can found searching ?adjustRtime. example using default parameters. matchLamaChromPeaks() function facilitates assessment well lamas correspond chromatographic peaks file. extract matched results using matchedRtimes() function. used later evaluate alignment. Now can adjust retention time LC-MS/MS dataset using adjustRtime() function.","code":"f <- sampleData(lcms1)$phenotype f[f != \"QC\"] <- NA lcms1 <- filterFeatures(lcms1, PercentMissingFilter(threshold = 0, f = f),                         filled = FALSE) 3694 features were removed lcms1_mz_rt <- featureDefinitions(lcms1)[, c(\"mzmed\",\"rtmed\")] head(lcms1_mz_rt) mzmed    rtmed FT0001 50.98979 203.6001 FT0002 51.05904 191.1675 FT0003 51.98657 203.1467 FT0004 53.02036 203.2343 FT0005 53.52080 203.1936 FT0007 54.01010 235.9032 nrow(lcms1_mz_rt) [1] 5374 lp <- LamaParama(lamas = lcms1_mz_rt, method = \"loess\", span = 0.5,                  outlierTolerance = 3, zeroWeight = 10, ppm =20,                  tolerance = 0, toleranceRt = 20, bs = \"tp\") lp <- matchLamasChromPeaks(lcms2, param = lp) ref_vs_obs <- matchedRtimes(lp) #' input into `adjustRtime()` lcms2 <- adjustRtime(lcms2, param = lp) lcms2 <- applyAdjustedRtime(lcms2)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"evaluation","dir":"Articles","previous_headings":"","what":"Evaluation","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"extract base peak chromatogram (BPC) aligned object: evaluate performance alignment process, generate plots comparing alignment reference dataset (black) LC-MS data (red) (blue) adjustment.   Although overall matching imperfect due initial sample issues, certain regions show significant improvement. alignment signal’s start particularly well done. Specifically, regions right 150 seconds show substantial improvement. visualization distribution chromatographic peaks matched anchor peaks (lamas) Sample . red vertical lines represent positions matched peaks.  quantitatively assess quality alignment, compute distance chromatographic peaks LC-MS data anchor peaks (Lamas) alignment.  Furthermore, detailed examination matching model used fitting file possible. Numerical information can obtained using summarizeLamaMatch() function. , percentage chromatographic peaks utilized alignment can computed relative total number peaks file. Additionally, feasible directly plot() param object file interest, showcasing distribution chromatographic peaks along fitted model line.","code":"#' evaluate the results with BPC bpc2_adj <- chromatogram(lcms2, aggregationFun = \"max\",                          msLevel = 1) #' BPC of sample A par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1,1]), intensity(bpc2[1,1]),        type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 1], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1,1]), intensity(bpc2_adj[1,1]),        type = \"l\",        col = \"#0000ff80\") #' BPC of sample B par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 2], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 2], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1, 2]), intensity(bpc2_adj[1, 2]), type = \"l\",        col = \"#0000ff80\") #' BPC of the first sample with matches to lamas overlay par(mfrow = c(1, 1)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Distribution CP matched to Lamas\",      lwd = 1.5,      peakType = \"none\") points(rtime(bpc2_adj[1, 1]), intensity(bpc2_adj[1, 1]), type = \"l\",        col = \"#0000ff80\") grid() abline(v = ref_vs_obs[[1]]$obs, col = \"#c4114510\") # Extract data for sample 3 directly ref_obs_sample_1 <- ref_vs_obs[[\"1\"]]  # Calculate distances before and after alignment dist_before <- abs(ref_obs_sample_1$obs - ref_obs_sample_1$ref) dist_after <- abs(chromPeaks(lcms2)[ref_obs_sample_1$chromPeaksId,                                              \"rt\"] - ref_obs_sample_1$ref)  # Create a data frame for plotting distances <- data.frame(   Distance = c(dist_before, dist_after),   Alignment = rep(c(\"Before\", \"After\"), each = length(dist_before)) )  # Set factor levels for Alignment to ensure correct order distances$Alignment <- factor(distances$Alignment, levels = c(\"Before\", \"After\"))  # Plot distances between anchor peaks between the two runs before and after alignment. vioplot(Distance ~ Alignment, data = distances, xlab = \"\",         rectCol = \"#c4114580\",         lineCol = \"white\",         col=\"#17138fe8\",         border = \"white\",         ylab = \"Distance (s)\",         main = \"Distance to Anchor Peaks: Before vs. After Alignment\") #' Access summary of matches and model information summary <- summarizeLamaMatch(lp) summary Total_peaks Matched_peaks Total_lamas Model_summary 1        6832          1825        5374  1666, c(.... 2        6860          1785        5374  1617, c(.... 3        7588          2082        5374  1869, c(.... #' Coverage for each file summary$Matched_peaks / summary$Total_peaks * 100 [1] 26.71253 26.02041 27.43806 #' Access the information on the model of for the first file summary$Model_summary[[1]] Call: loess(formula = ref ~ obs, data = rt_map, weights = weights,     span = span)  Number of Observations: 1666 Equivalent Number of Parameters: 7.38 Residual Standard Error: 2.315 Trace of smoother matrix: 8.13  (exact)  Control settings:   span     :  0.5   degree   :  2   family   :  gaussian   surface  :  interpolate     cell = 0.2   normalize:  TRUE  parametric:  FALSE drop.square:  FALSE #' Plot obs vs. lcms1 with fitting line plot(lp, index = 1L, main = \"ChromPeaks versus Lamas for sample A\",      colPoint = \"red\") abline(0, 1, lty = 3, col = \"grey\") grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"visualizing-alignment-quality","dir":"Articles","previous_headings":"Introduction","what":"Visualizing Alignment Quality","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"evaluate performance alignment process, generate plots comparing alignment reference dataset (black) LC-MS data (red) (blue) adjustment.   Although overall matching imperfect due initial sample issues, certain regions show significant improvement. alignment signal’s start particularly well done. Specifically, regions right 150 seconds show substantial improvement. visualization distribution chromatographic peaks matched anchor peaks (lamas) Sample . red vertical lines represent positions matched peaks.","code":"#' BPC of sample A par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1,1]), intensity(bpc2[1,1]),        type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 1], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1,1]), intensity(bpc2_adj[1,1]),        type = \"l\",        col = \"#0000ff80\") #' BPC of sample B par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 2], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 2], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1, 2]), intensity(bpc2_adj[1, 2]), type = \"l\",        col = \"#0000ff80\") #' BPC of the first sample with matches to lamas overlay par(mfrow = c(1, 1)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Distribution CP matched to Lamas\",      lwd = 1.5,      peakType = \"none\") points(rtime(bpc2_adj[1, 1]), intensity(bpc2_adj[1, 1]), type = \"l\",        col = \"#0000ff80\") grid() abline(v = ref_vs_obs[[1]]$obs, col = \"#c4114510\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"quantitative-evaluation-of-alignment","dir":"Articles","previous_headings":"Introduction","what":"Quantitative Evaluation of Alignment","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"quantitatively assess quality alignment, compute distance chromatographic peaks LC-MS data anchor peaks (Lamas) alignment.  Furthermore, detailed examination matching model used fitting file possible. Numerical information can obtained using summarizeLamaMatch() function. , percentage chromatographic peaks utilized alignment can computed relative total number peaks file. Additionally, feasible directly plot() param object file interest, showcasing distribution chromatographic peaks along fitted model line.","code":"# Extract data for sample 3 directly ref_obs_sample_1 <- ref_vs_obs[[\"1\"]]  # Calculate distances before and after alignment dist_before <- abs(ref_obs_sample_1$obs - ref_obs_sample_1$ref) dist_after <- abs(chromPeaks(lcms2)[ref_obs_sample_1$chromPeaksId,                                              \"rt\"] - ref_obs_sample_1$ref)  # Create a data frame for plotting distances <- data.frame(   Distance = c(dist_before, dist_after),   Alignment = rep(c(\"Before\", \"After\"), each = length(dist_before)) )  # Set factor levels for Alignment to ensure correct order distances$Alignment <- factor(distances$Alignment, levels = c(\"Before\", \"After\"))  # Plot distances between anchor peaks between the two runs before and after alignment. vioplot(Distance ~ Alignment, data = distances, xlab = \"\",         rectCol = \"#c4114580\",         lineCol = \"white\",         col=\"#17138fe8\",         border = \"white\",         ylab = \"Distance (s)\",         main = \"Distance to Anchor Peaks: Before vs. After Alignment\") #' Access summary of matches and model information summary <- summarizeLamaMatch(lp) summary Total_peaks Matched_peaks Total_lamas Model_summary 1        6832          1825        5374  1666, c(.... 2        6860          1785        5374  1617, c(.... 3        7588          2082        5374  1869, c(.... #' Coverage for each file summary$Matched_peaks / summary$Total_peaks * 100 [1] 26.71253 26.02041 27.43806 #' Access the information on the model of for the first file summary$Model_summary[[1]] Call: loess(formula = ref ~ obs, data = rt_map, weights = weights,     span = span)  Number of Observations: 1666 Equivalent Number of Parameters: 7.38 Residual Standard Error: 2.315 Trace of smoother matrix: 8.13  (exact)  Control settings:   span     :  0.5   degree   :  2   family   :  gaussian   surface  :  interpolate     cell = 0.2   normalize:  TRUE  parametric:  FALSE drop.square:  FALSE #' Plot obs vs. lcms1 with fitting line plot(lp, index = 1L, main = \"ChromPeaks versus Lamas for sample A\",      colPoint = \"red\") abline(0, 1, lty = 3, col = \"grey\") grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"tutorial demonstrated align LC-MS LC-MS/MS datasets correct retention time shifts, crucial handling data different runs platforms. preprocessed data, detected chromatographic peaks, used landmark features (lamas) QC samples adjust retention times via adjustRtime() function. Visual comparisons base peak chromatograms alignment, along distance calculations, showed clear improvements RT synchronization. Ultimately, aligning chromatographic data ensures subsequent analyses, feature extraction statistical comparisons, based consistent time points, improving data quality reliability. tutorial outlined end--end workflow can adapted various LC-MS-based metabolomics studies, helping researchers manage retention time variation effectively.","code":""},{"path":[]},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/dataset-investigation.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Dataset investigation: What to do when you get your data","text":", (amazing lab mate) finally finished data acquisition, now dataset hand. ’s next? Unfortunately, work isn’t yet. diving analysis, ’s crucial understand dataset . first step data analysis workflow, ensuring data good quality well-prepared preprocessing downstream analysis plan perform. vignette, present dataset used throughout different vignettes website. ’s far perfect dataset, actually mirrors reality datasets ’ll encounter research. issues indeed specific described dataset. However, purpose vignette encourage think critically data guide steps can help avoid spending hours analysis, realize later samples features removed flagged earlier .","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/dataset-investigation.html","id":"dataset-description","dir":"Articles","previous_headings":"","what":"Dataset Description","title":"Dataset investigation: What to do when you get your data","text":"workflow, two datasets used: LC-MS-based (MS1 level ) untargeted metabolomics dataset quantify small polar metabolites human plasma samples. additional LC-MS/MS dataset selected samples former study identification annotation significant features. samples randomly selected larger study aimed identifying metabolites varying abundances individuals suffering cardiovascular disease (CVD) healthy controls (CTR). subset analyzed includes data three CVD patients, three CTR individuals, four quality control (QC) samples. QC samples, representing pooled serum sample large cohort, measured repeatedly throughout experiment monitor signal stability. data metadata workflow available MetaboLights database ID: MTBLS8735. detailed materials methods used sample analysis also available MetaboLights entry. particularly important understanding analysis parameters used. noted samples analyzed using ultra-high-performance liquid chromatography (UHPLC) coupled Q-TOF mass spectrometer (TripleTOF 5600+), chromatographic separation achieved using hydrophilic interaction liquid chromatography (HILIC). Consider moving visualizations end--end vignette clearer understanding dataset. Provide -depth visualizations explore understand dataset quality. Compare pool lc-ms pool lc-ms/ms show better separation second run.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/install_v0.html","id":"running-workflows-locally","dir":"Articles","previous_headings":"","what":"Running workflows locally","title":"Install","text":"install computer packages necessary workflows run code follow: get error message downloading form GitHub might due expired token. Remove using code try install :","code":"install.packages(\"BiocManager\") BiocManager::install('RforMassSpectrometry/MsIO', ask = FALSE,                       dependencies = TRUE)  BiocManager::install(\"RforMassSpectrometry/metabonaut\",                      dependencies = TRUE, ask = FALSE, update = TRUE) #See if any token is there: gitcreds::gitcreds_get()  # Delete them: gitcreds::gitcreds_delete()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/install_v0.html","id":"docker-image","dir":"Articles","previous_headings":"","what":"Docker image","title":"Install","text":"vignettes files along R runtime environment including required packages RStudio (Posit) editor bundled docker container. installation, docker container can run computer code examples vignettes can evaluated within environment (without need install additional packages files). don’t already , install docker. Find installation information . Get docker image tutorial e.g. command line : Start docker container, either Docker Desktop, command line Enter http://localhost:8787 web browser log username rstudio password bioc. RStudio server version: open Quarto files vignettes folder evaluate R code blocks document.","code":"docker pull rformassspectrometry/metabonaut:latest docker run -e PASSWORD=bioc -p 8787:8787 rformassspectrometry/metabonaut:latest"},{"path":"https://rformassspectrometry.github.io/metabonaut/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Philippine Louail. Author, maintainer.           ORCID: 0009-0007-5429-6846 Anna Tagliaferri. Contributor.           ORCID: 0009-0001-4044-4285 Vinicius Verri Hernandes. Contributor.           ORCID: 0000-0002-3057-6460 Daniel Marques de Sá e Silva. Contributor.           ORCID: 0000-0002-9674-042X Johannes Rainer. Author.           ORCID: 0000-0002-6977-7147","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Philippine Louail, & Johannes Rainer. (2024). Streamlining LC-MS/MS Data Analysis R Open-Source xcms RforMassSpectrometry: End--End Workflow (Version v1).Zenodo. https://doi.org/10.5281/zenodo.11370612","code":"@Manual{,   title = {Streamlining LC-MS/MS Data Analysis in R with Open-Source xcms and RforMassSpectrometry: An End-to-End Workflow},   author = {Philippine Louail and Johannes Rainer},   publisher = {Zenodo},   year = {2024},   month = {may},   version = {v1.1.0},   doi = {10.5281/zenodo.11370612},   url = {https://doi.org/10.5281/zenodo.11370612}, }"},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"lets-explore-and-learn-to-analyze-untargeted-metabolomics-data","dir":"","previous_headings":"","what":"Exploring and Analyzing LC-MS Data","title":"Exploring and Analyzing LC-MS Data","text":"Welcome Metabonaut! 🧑‍🚀 initiative presents series workflows based small LC-MS/MS dataset, utilizing R Bioconductor packages. Throughout workflows, demonstrate adapt various algorithms specific datasets seamlessly integrate R packages ensure efficient, reproducible processing. primary workflow “Complete End--End LC-MS/MS Metabolomic Data Analysis”. full R code examples, along detailed descriptions, available end--end-untargeted-metabolomics.qmd file. file can opened RStudio, allowing execute individual R command. vignettes website interlinked, can find detailed description dataset used throughout . strive reproducibility. workflows designed remain stable time, allowing run vignettes together one comprehensive “super-vignette”. major change document smaller updates check News","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"for-r-beginners","dir":"","previous_headings":"","what":"For R beginners","title":"Exploring and Analyzing LC-MS Data","text":"tutorials provided assume users basic knowledge R RMarkdown. ’re unfamiliar either, recommend completing short tutorial help test code adapt data. vignettes written Quarto format, learn go , farily new format, functionallity shared RMarkdown format, therefore learning can usefull . basic R course documentation, recommand check website try interactive course fun introduction basic R programming. cheatsheet also help.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"known-issues","dir":"","previous_headings":"","what":"Known Issues","title":"Exploring and Analyzing LC-MS Data","text":"just beginning Metabonaut journey, website still refined. ’re actively addressing ongoing issues. ’re aware problem, ’ll list . Currently, known issues code. encounter , please ensure latest versions required packages (detailed ). issue persists, please report reproducible example GitHub . encounter issues, don’t hesitate let us know!","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"Exploring and Analyzing LC-MS Data","text":"contributions, please see RforMassSpectrometry contributions guideline.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Exploring and Analyzing LC-MS Data","text":"Please review RforMassSpectrometry Code Conduct.","code":""},{"path":[]},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.5","title":"metabonaut 0.0","text":"Require MsIO version 0.0.8 allow reading stored MsBackendMetaboLights objects. simplify import previously stored result object avoid using variable name param every parameter object","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-1","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.4","title":"metabonaut 0.0","text":"Required alabaster.se. Removal Spectra data depth visualisation move Data investigation vignette Removal internal standard matching features Normalization part. Save load lcms1 res object end--end workflow used Seamless Alignment vignette. Using MsIO alabaster.se","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-2","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.3","title":"metabonaut 0.0","text":"Require xcms version 4.3.4 install package github.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-3","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.2","title":"metabonaut 0.0","text":"Switch Quarto instead Rmarkdown Addition Alignment reference dataset vignette Addition Data investigation vignette Addition Install vignette","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-4","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.1","title":"metabonaut 0.0","text":"Addition basic files workflow package. Addition end--end vignette.","code":""}]
+[{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"present workflow describes steps analysis LC-MS/MS experiment, includes preprocessing raw data generate abundance matrix features various samples, followed data normalization, differential abundance analysis finally annotation features metabolites. Note also alternative analysis options R packages used different steps examples mentioned throughout workflow.  Steps end--end workflow possible alternatives","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-description","dir":"Articles","previous_headings":"","what":"Data description","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"See data description vignette detailed explanation dataset go workflow general tips done first get data.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"packages-needed","dir":"Articles","previous_headings":"","what":"Packages needed","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"workflow therefore based following dependencies:","code":"## Data Import and handling library(readxl) library(MsExperiment) library(MsIO) library(alabaster.se) library(MsBackendMetaboLights) library(SummarizedExperiment)  ## Preprocessing of LC-MS data library(xcms) library(Spectra) library(MetaboCoreUtils)  ## Statistical analysis library(limma) # Differential abundance library(matrixStats) # Summaries over matrices  ## Visualisation library(pander) library(RColorBrewer) library(pheatmap) library(vioplot) library(ggfortify)   # Plot PCA library(gridExtra)   # To arrange multiple ggplots into single plots  ## Annotation library(AnnotationHub) # Annotation resources library(CompoundDb)    # Access small compound annotation data. library(MetaboAnnotation) # Functionality for metabolite annotation."},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-import","dir":"Articles","previous_headings":"","what":"Data import","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Note different equipment generate various file extensions, conversion step might needed beforehand, though apply dataset. Spectra package supports variety ways store retrieve MS data, including mzML, mzXML, CDF files, simple flat files, database systems. necessary, several tools, ProteoWizard’s MSConvert, can used convert files .mzML format (Chambers et al. 2012). show extract dataset MetaboLigths database load MsExperiment object. information load data MetaboLights database, refer vignette. type data loading, check xcms vignette specific vignette created data import soon. next configure parallel processing setup. functions xcms package allow per-sample parallel processing, can improve performance analysis, especially large data sets. xcms packages RforMassSpectrometry package ecosystem use parallel processing setup configured BiocParallel Bioconductor package. code use fork-based parallel processing unix system, socket-based parallel processing Windows operating system.","code":"param <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                            assayName = paste0(\"a_MTBLS8735_LC-MS_positive_\",                                               \"hilic_metabolite_profiling.txt\"),                            filePattern = \".mzML\")  lcms1 <- readMsObject(MsExperiment(),                       param,                       keepOntology = FALSE,                       keepProtocol = FALSE,                       simplify = TRUE) #' Set up parallel processing using 2 cores if (.Platform$OS.type == \"unix\") {     register(MulticoreParam(2)) } else {     register(SnowParam(2)) }"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-organisation","dir":"Articles","previous_headings":"","what":"Data organisation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"experimental data now represented MsExperiment object MsExperiment package. MsExperiment object container metadata spectral data provides manages also linkage samples spectra. provide brief overview data structure content. sampleData() function extracts sample information object. next extract data use pander package render show information Table 1 . Throughout document use R pipe operator (|>) avoid nested function calls hence improve code readability. sampleData() output MetaboLights always ideal direct easy access data. therefore rename transform user-friendly way. user can add, transform remove column want using base R functionalities. Table 1. Samples data set. Table 2. Simplified sample data. 11 samples data set. abbreviations essential proper interpretation metadata information: \"QC\": Quality control sample (pool serum samples external, large cohort). \"CVD\": Sample individual cardiovascular disease. \"CTR\": Sample presumably healthy control. sample_name: arbitrary name/identifier sample. age: (rounded) age individuals. injection_index: index representing order (position) individual sample measured (injected) within LC-MS measurement run experiment. define colors sample groups based sample group using RColorBrewer package: MS data experiment stored Spectra object (Spectra Bioconductor package) within MsExperiment object can accessed using spectra() function. element object spectrum - organised linearly combined Spectra object one (ordered retention time samples). access dataset’s Spectra object summarize available information provide, among things, total number spectra data set.","code":"lcms1 Object of class MsExperiment  Spectra: MS1 (17210)  Experiment data: 10 sample(s)  Sample data links:   - spectra: 10 sample(s) to 17210 element(s). sampleData(lcms1)[, c(\"Derived_Spectral_Data_File\",                       \"Characteristics[Sample type]\",                       \"Factor Value[Phenotype]\",                       \"Sample Name\",                       \"Factor Value[Age]\")] |>     kable(format = \"pipe\") # Let's rename the column for easier access colnames(sampleData(lcms1)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  # Add \"QC\" to the phenotype of the QC samples sampleData(lcms1)$phenotype[sampleData(lcms1)$sample_name == \"POOL\"] <- \"QC\" sampleData(lcms1)$sample_name[sampleData(lcms1)$sample_name == \"POOL\" ] <- c(\"POOL1\", \"POOL2\", \"POOL3\", \"POOL4\")  #  Add injection index column sampleData(lcms1)$injection_index <- seq_len(nrow(sampleData(lcms1)))  #let's look at the updated sample data sampleData(lcms1)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\",                      \"injection_index\")] |>     kable(format = \"pipe\") #' Define colors for the different phenotypes col_phenotype <- brewer.pal(9, name = \"Set1\")[c(9, 5, 4)] names(col_phenotype) <- c(\"QC\", # grey                           \"CVD\", # orange                           \"CTR\") # purple col_sample <- col_phenotype[sampleData(lcms1)$phenotype] #' Access Spectra Object spectra(lcms1) MSn data (Spectra) with 17210 spectra in a MsBackendMetaboLights backend:         msLevel     rtime scanIndex       <integer> <numeric> <integer> 1             1     0.274         1 2             1     0.553         2 3             1     0.832         3 4             1     1.111         4 5             1     1.390         5 ...         ...       ...       ... 17206         1   479.052      1717 17207         1   479.331      1718 17208         1   479.610      1719 17209         1   479.889      1720 17210         1   480.168      1721  ... 36 more variables/columns.  file(s): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML  ... 7 more files"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-visualization-and-general-quality-assessment","dir":"Articles","previous_headings":"","what":"Data visualization and general quality assessment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Effective visualization paramount inspecting assessing quality MS data. general overview LC-MS data, can: Combine mass peaks (MS1) spectra sample single spectrum mass peak represents maximum signal mass peaks similar m/z. spectrum might called Base Peak Spectrum (BPS), providing information abundant ions sample. Aggregate mass peak intensities spectrum, resulting Base Peak Chromatogram (BPC). BPC shows highest measured intensity distinct retention time (hence spectrum) thus orthogonal BPS. Sum mass peak intensities spectrum create Total Ion Chromatogram (TIC). Compare BPS samples experiment evaluate similarity ion content. Compare BPC samples experiment identify samples similar dissimilar chromatographic signal. addition general data evaluation visualization, also crucial investigate specific signal e.g. internal standards compounds/ions known present samples. providing reliable reference, internal standards help achieve consistent accurate analytical results. LC signal variable unstable nature LC, show BPC similarity. depth inspection discussion spectra chromatrograhic data cna found data exploration vignette. chromatogram() function facilitates extraction intensities along retention time. However, access chromatographic information currently efficient seamless spectral information. Work underway develop/improve infrastructure chromatographic data new Chromatograms object aimed flexible user-friendly Spectra object. visualizing LC-MS data, BPC TIC serves valuable tool assess performance liquid chromatography across various samples experiment. case, extract BPC data create plot. BPC captures maximum peak signal spectrum data file plots information retention time spectrum y-axis. BPC can extracted using chromatogram function. setting parameter aggregationFun = \"max\", instruct function report maximum signal per spectrum. Conversely, setting aggregationFun = \"sum\", sums intensities spectrum, thereby creating TIC.  Figure 5. BPC samples colored phenotype. 240 seconds signal seems measured. Thus, filter data removing part well first 10 seconds measured LC run.  Figure 6. BPC filtering retention time. Initially, examined entire BPC subsequently filtered based desired retention times. results smaller file size also facilitates straightforward interpretation BPC. final plot illustrates BPC sample colored phenotype, providing insights signal measured along retention times sample. reveals points compounds eluted LC column. essence, BPC condenses three-dimensional LC-MS data (m/z retention time intensity) two dimensions (retention time intensity). can also compare similarities BPCs heatmap. retention times however identical different samples. Thus bin() chromatographic signal per sample along retention time axis bins two seconds resulting data number bins/data points. can calculate pairwise similarities data vectors using cor() function visualize result using pheatmap().  Figure 7. Heatmap BPC similarities. heatmap reinforces exploration spectra data showed, strong separation QC study samples. important bear mind later analyses. Throughout entire process, crucial reference points within dataset, well-known ions. experiments nowadays include internal standards (), case . strongly recommend using visualization throughout entire analysis. experiment, set 15 spiked samples. reviewing signal , selected two guide analysis process. However, also advise plot evaluate ions steps. illustrate , generate Extracted Ion Chromatograms (EIC) selected test ions. restricting MS data intensities within restricted, small m/z range selected retention time window, EICs expected contain signal single type ion. expected m/z retention times set determined different experiment. Additionally, cases internal standards available, commonly present ions sample matrix can serve suitable alternatives. Ideally, compounds distributed across entire retention time range experiment. Table 4. Internal standard list respective m/z expected retention time [s]. plot EICs isotope labeled cystine methionine.  Figure 9. EIC cystine methionine. can observe clear concentration difference QCs study samples isotope labeled cystine ion. Meanwhile, labeled methionine internal standard exhibits discernible signal amidst noise noticeable retention time shift samples.","code":"#' Extract and plot BPC for full data bpc <- chromatogram(lcms1, aggregationFun = \"max\")  plot(bpc, col = paste0(col_sample, 80), main = \"BPC\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE,        bty = \"n\") #' Filter the data based on retention time lcms1 <- filterRt(lcms1, c(10, 240)) Filter spectra bpc <- chromatogram(lcms1, aggregationFun = \"max\") #' Plot after filtering plot(bpc, col = paste0(col_sample, 80),      main = \"BPC after filtering retention time\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") #' Total ion chromatogram tic <- chromatogram(lcms1, aggregationFun = \"sum\") |>   bin(binSize = 2)  #' Calculate similarity (Pearson correlation) between BPCs ticmap <- do.call(cbind, lapply(tic, intensity)) |>   cor()  rownames(ticmap) <- colnames(ticmap) <- sampleData(lcms1)$sample_name ann <- data.frame(phenotype = sampleData(lcms1)[, \"phenotype\"]) rownames(ann) <- rownames(ticmap)  #' Plot heatmap pheatmap(ticmap, annotation_col = ann,          annotation_colors = list(phenotype = col_phenotype)) #' Load our list of standard intern_standard <- read.delim(\"intern_standard_list.txt\")  # Extract EICs for the list eic_is <- chromatogram(     lcms1,     rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),     mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")]))  #' Add internal standard metadata fData(eic_is)$mz <- intern_standard$mz fData(eic_is)$rt <- intern_standard$RT fData(eic_is)$name <- intern_standard$name fData(eic_is)$abbreviation <- intern_standard$abbreviation rownames(fData(eic_is)) <- intern_standard$abbreviation  fdata <- fData(eic_is)  #' Summary of IS information fData(eic_is)[, c(\"name\", \"mz\", \"rt\")] |>     kable(format = \"pipe\") #' Extract the two IS from the chromatogram object. eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"]  #' plot both EIC par(mfrow = c(1, 2), mar = c(4, 2, 2, 0.5)) plot(eic_cystine, main = fData(eic_cystine)$name, cex.axis = 0.8,      cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_cystine)$rt, col = \"red\", lty = 3)  plot(eic_met, main = fData(eic_met)$name, cex.axis = 0.8, cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_met)$rt, col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1,        bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"chromatographic-data-visualization-bpc-and-tic","dir":"Articles","previous_headings":"","what":"Chromatographic Data Visualization: BPC and TIC","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"chromatogram() function facilitates extraction intensities along retention time. However, access chromatographic information currently efficient seamless spectral information. Work underway develop/improve infrastructure chromatographic data new Chromatograms object aimed flexible user-friendly Spectra object. visualizing LC-MS data, BPC TIC serves valuable tool assess performance liquid chromatography across various samples experiment. case, extract BPC data create plot. BPC captures maximum peak signal spectrum data file plots information retention time spectrum y-axis. BPC can extracted using chromatogram function. setting parameter aggregationFun = \"max\", instruct function report maximum signal per spectrum. Conversely, setting aggregationFun = \"sum\", sums intensities spectrum, thereby creating TIC.  Figure 5. BPC samples colored phenotype. 240 seconds signal seems measured. Thus, filter data removing part well first 10 seconds measured LC run.  Figure 6. BPC filtering retention time. Initially, examined entire BPC subsequently filtered based desired retention times. results smaller file size also facilitates straightforward interpretation BPC. final plot illustrates BPC sample colored phenotype, providing insights signal measured along retention times sample. reveals points compounds eluted LC column. essence, BPC condenses three-dimensional LC-MS data (m/z retention time intensity) two dimensions (retention time intensity). can also compare similarities BPCs heatmap. retention times however identical different samples. Thus bin() chromatographic signal per sample along retention time axis bins two seconds resulting data number bins/data points. can calculate pairwise similarities data vectors using cor() function visualize result using pheatmap().  Figure 7. Heatmap BPC similarities. heatmap reinforces exploration spectra data showed, strong separation QC study samples. important bear mind later analyses.","code":"#' Extract and plot BPC for full data bpc <- chromatogram(lcms1, aggregationFun = \"max\")  plot(bpc, col = paste0(col_sample, 80), main = \"BPC\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE,        bty = \"n\") #' Filter the data based on retention time lcms1 <- filterRt(lcms1, c(10, 240)) Filter spectra bpc <- chromatogram(lcms1, aggregationFun = \"max\") #' Plot after filtering plot(bpc, col = paste0(col_sample, 80),      main = \"BPC after filtering retention time\", lwd = 1.5) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") #' Total ion chromatogram tic <- chromatogram(lcms1, aggregationFun = \"sum\") |>   bin(binSize = 2)  #' Calculate similarity (Pearson correlation) between BPCs ticmap <- do.call(cbind, lapply(tic, intensity)) |>   cor()  rownames(ticmap) <- colnames(ticmap) <- sampleData(lcms1)$sample_name ann <- data.frame(phenotype = sampleData(lcms1)[, \"phenotype\"]) rownames(ann) <- rownames(ticmap)  #' Plot heatmap pheatmap(ticmap, annotation_col = ann,          annotation_colors = list(phenotype = col_phenotype))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"chromatographic-data-visualization-eics","dir":"Articles","previous_headings":"","what":"Chromatographic Data Visualization: EICs","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Throughout entire process, crucial reference points within dataset, well-known ions. experiments nowadays include internal standards (), case . strongly recommend using visualization throughout entire analysis. experiment, set 15 spiked samples. reviewing signal , selected two guide analysis process. However, also advise plot evaluate ions steps. illustrate , generate Extracted Ion Chromatograms (EIC) selected test ions. restricting MS data intensities within restricted, small m/z range selected retention time window, EICs expected contain signal single type ion. expected m/z retention times set determined different experiment. Additionally, cases internal standards available, commonly present ions sample matrix can serve suitable alternatives. Ideally, compounds distributed across entire retention time range experiment. Table 4. Internal standard list respective m/z expected retention time [s]. plot EICs isotope labeled cystine methionine.  Figure 9. EIC cystine methionine. can observe clear concentration difference QCs study samples isotope labeled cystine ion. Meanwhile, labeled methionine internal standard exhibits discernible signal amidst noise noticeable retention time shift samples.","code":"#' Load our list of standard intern_standard <- read.delim(\"intern_standard_list.txt\")  # Extract EICs for the list eic_is <- chromatogram(     lcms1,     rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),     mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")]))  #' Add internal standard metadata fData(eic_is)$mz <- intern_standard$mz fData(eic_is)$rt <- intern_standard$RT fData(eic_is)$name <- intern_standard$name fData(eic_is)$abbreviation <- intern_standard$abbreviation rownames(fData(eic_is)) <- intern_standard$abbreviation  fdata <- fData(eic_is)  #' Summary of IS information fData(eic_is)[, c(\"name\", \"mz\", \"rt\")] |>     kable(format = \"pipe\") #' Extract the two IS from the chromatogram object. eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"]  #' plot both EIC par(mfrow = c(1, 2), mar = c(4, 2, 2, 0.5)) plot(eic_cystine, main = fData(eic_cystine)$name, cex.axis = 0.8,      cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_cystine)$rt, col = \"red\", lty = 3)  plot(eic_met, main = fData(eic_met)$name, cex.axis = 0.8, cex.main = 0.8,      col = paste0(col_sample, 80)) grid() abline(v = fData(eic_met)$rt, col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1,        bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-preprocessing","dir":"Articles","previous_headings":"","what":"Data preprocessing","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Preprocessing stands inaugural step analysis untargeted LC-MS. characterized 3 main stages: chromatographic peak detection, retention time shift correction (alignment) correspondence results features defined. primary objective preprocessing quantification signals ions measured sample, addressing potential retention time drifts samples, ensuring alignment quantified signals across samples within experiment. final result LC-MS data preprocessing numeric matrix abundances quantified entities samples experiment. initial preprocessing step involves detecting intensity peaks along retention time axis, called chromatographic peaks. achieve , employ findChromPeaks() function within xcms. function supports various algorithms peak detection, can selected configured respective parameter objects. preferred algorithm case, CentWave, utilizes continuous wavelet transformation (CWT)-based peak detection (Tautenhahn, Böttcher, Neumann 2008). method known effectiveness handling non-Gaussian shaped chromatographic peaks peaks varying retention time widths, commonly encountered HILIC separations. apply CentWave algorithm default settings extracted ion chromatogram cystine methionine ions evaluate results. CentWave highly performant algorithm, requires costumized dataset. implies parameters fine-tuned based user’s data. example serves clear motivation users familiarize various parameters need adapt data set. discuss main parameters can easily adjusted suit user’s dataset: peakwidth: Specifies minimal maximal expected width peaks retention time dimension. Highly dependent chromatographic settings used. ppm: maximal allowed difference mass peaks’ m/z values (parts-per-million) consecutive scans consider representing signal ion. integrate: parameter defines integration method. , primarily use integrate = 2 integrates also signal chromatographic peak’s tail considered accurate developers. determine peakwidth, recommend users refer previous EICs estimate range peak widths observe dataset. Ideally, examining multiple EICs goal. dataset, peak widths appear around 2 10 seconds. advise choosing range wide narrow peakwidth parameter can lead false positives negatives. determine ppm, deeper analysis dataset needed. clarified ppm depends instrument, users necessarily input vendor-advertised ppm. ’s determine accurately possible: following steps involve generating highly restricted MS area single mass peak per spectrum, representing cystine ion. m/z peaks extracted, absolute difference calculated finally expressed ppm. therefore, choose value close maximum within range parameter ppm, .e., 15 ppm. can now perform chromatographic peak detection adapted settings EICs. important note , properly estimate background noise, sufficient data points outside chromatographic peak need present. generally problem peak detection performed full LC-MS data set, peak detection EICs retention time range EIC needs sufficiently wide. function fails find peak EIC, initial troubleshooting step increase range. Additionally, signal--noise threshold snthresh reduced peak detection EICs, within small retention time range, enough signal present properly estimate background noise. Finally, case MS1 data points per peaks, setting CentWave’s advanced parameter extendLengthMSW TRUE can help peak detection. customized parameters, chromatographic peak detected sample. , use plot() function EICs visualize results.  Figure 11. Chromatographic peak detection EICs. can see peak seems ot detected sample ions. indicates custom settings seem thus suitable dataset. now proceed apply entire dataset, extracting EICs ions evaluate confirm chromatographic peak detection worked expected. Note: revert value parameter snthresh default, , mentioned , background noise estimation reliable performed full data set. Parameter chunkSize findChromPeaks() defines number data files loaded memory processed simultaneously. parameter thus allows fine-tune memory demand well performance chromatographic peak detection step. plot EICs two selected internal standards evaluate chromatographic peak detection results.  Figure 12. Chromatographic peak detection EICs processing. Peaks seem detected properly samples ions. indicates peak detection process entire dataset successful. identification chromatographic peaks using CentWave algorithm can sometimes result artifacts, overlapping split peaks. address issue, refineChromPeaks() function utilized, conjunction MergeNeighboringPeaksParam, aims merging split peaks. show examples CentWave peak detection artifacts. examples pre-selected illustrate necessity next step:  Figure 13. Examples CentWave peak detection artifacts. cases signal presumably single type ion split two separate chromatographic peaks (indicated vertical line). MergeNeigboringPeaksParam allows combine split peaks. parameters algorithm defined : expandMz: Suggested kept relatively small (0.0015) prevent merging isotopes. expandRt: Usually set approximately half size average retention time width used chromatographic peak detection (case, 2.5 seconds). minProp: Used determine whether candidates merged. Chromatographic peaks overlapping m/z ranges (expanded side expandMz) tail--head distance retention time dimension less 2 * expandRt, signal higher minProp apex intensity chromatographic peak lower intensity, merged. Values parameter small avoid merging closely co-eluting ions, isomers. test settings EICs split peaks.  Figure 14. Examples CentWave peak detection artifacts merging. can observe artificially split peaks appropriately merged. Therefore, next apply settings entire dataset. peak merging, column \"merged\" result object’s chromPeakData() data frame can used evaluate chromatographic peaks result represent signal merged, originally identified chromatographic peaks. proceeding next preprocessing step generally suggested evaluate results chromatographic peak detection EICs e.g. internal standards compounds/ions known present samples. update EICs objects internal standards results chromatographic peak detection refinement. Additionally, evaluating comparing number identified chromatographic peaks samples data set can help spotting potentially problematic samples. count number chromatographic peaks per sample show numbers table. Table 5. Samples number identified chromatographic peaks. similar number chromatographic peaks identified within various samples data set. Additional options evaluate results chromatographic peak detection can implemented using plotChromPeaks() function summarizing results using base R commands. Despite using chromatographic settings conditions retention time shifts unavoidable. Indeed, performance instrument can change time, example due small variations environmental conditions, temperature pressure. shifts generally small samples measured within batch/measurement run, can considerable data experiment acquired across longer time period. evaluate presence shift extract plot BPC QC samples.  Figure 15. BPC QC samples. QC samples representing sample (pool) measured regular intervals measurement run experiment measured day. Still, small shifts can observed, especially region 100 150 seconds. facilitate proper correspondence signals across samples (hence definition LC-MS features), essential minimize differences retention times. Theoretically, proceed two steps: first select QC samples dataset first alignment , using -called anchor peaks. way can assume linear shift time, since always measuring sample different regular time intervals. Despite external QCs data set, still use subset-based alignment assuming retention time shifts independent different sample matrix (human serum plasma) instead mostly instrument-dependent. Note also possible manually specify anchor peaks, respectively retention times align data set external, reference, data set. information provided vignettes xcms package. calculating much adjust retention time samples, apply shift also study samples. xcms retention time alignment can performed using adjustRtime() function alignment algorithm. example use PeakGroups method (Smith et al. 2006) performs alignment minimizing differences retention times set anchor peaks different samples. method requires initial correspondence analysis match/group chromatographic peaks across samples algorithm selects anchor peaks alignment. initial correspondence, use PeakDensity approach (Smith et al. 2006) groups chromatographic peaks similar m/z retention time LC-MS features. parameters algorithm, can configured using PeakDensityParam object, sampleGroups, minFraction, binSize, ppm bw. binSize, ppm bw allow specify similar chromatographic peaks’ m/z retention time values need consider grouping feature. binSize ppm define required similarity m/z values. Within m/z bin (defined binSize ppm) areas along retention time axis high chromatographic peak density (considering peaks samples) identified, chromatographic peaks within regions considered grouping feature. High density areas identified using base R density() function, bw parameter: higher values define wider retention time areas, lower values require chromatographic peaks similar retention times. parameter can seen black line plot , corresponding smoothness density curve. Whether candidate peaks get grouped feature depends also parameters sampleGroups minFraction: sampleGroups provide, sample, sample group belongs . minFraction expected value 0 1 defining proportion samples within least one sample groups (defined sampleGroups) chromatographic peaks detected group feature. initial correspondence, parameters don’t need fully optimized. Selection dataset-specific parameter values described detail next section. dataset, use small values binSize ppm , importantly, also parameter bw, since data set ultra high performance (UHP) LC setup used. minFraction use high value (0.9) ensure features defined chromatographic peaks present almost samples one sample group (can used anchor peaks actual alignment). base alignment later QC samples hence define sampleGroups binary variable grouping samples either study, QC group.  Figure 16. Initial correspondence analysis. PeakGroups-based alignment can next performed using adjustRtime() function PeakGroupsParam parameter object. parameters algorithm : subsetAdjust subset: Allows subset alignment. base retention time alignment QC samples, .e., retention time shifts estimated based repeatedly measured samples. resulting adjustment applied entire data. data sets QC samples (e.g. sample pools) measured repeatedly, strongly suggest use method. Note also subset-based alignment samples ordered injection index (.e., order measured measurement run). minFraction: value 0 1 defining proportion samples (full data set, data subset defined subset) chromatographic peak identified use anchor peak. contrast PeakDensityParam parameter used define proportion within sample group. span: PeakGroups method allows, depending data, adjust regions along retention time axis differently. enable local alignments LOESS function used parameter defines degree smoothing function. Generally, values 0.4 0.6 used, however, suggested evaluate alignment results eventually adapt parameters result satisfactory. perform alignment data set based retention times anchor peaks defined subset QC samples. Alignment adjusted retention times spectra data set, well retention times identified chromatographic peaks. alignment performed, user evaluate results using plotAdjustedRtime() function. function visualizes difference adjusted raw retention time sample y-axis along adjusted retention time x-axis. Dot points represent position used anchor peak along retention time axis. optimal alignment areas along retention time axis, anchor peaks scattered retention time dimension.  Figure 17. Retention time alignment results. samples present data set measured within measurement run, resulting small retention time shifts. Therefore, little adjustments needed performed (shifts maximum 1 second can seen plot ). Generally, magnitude adjustment seen plots match expectation analyst. can also compare BPC alignment. get original data, .e. raw retention times, can use dropAdjustedRtime() function:  Figure 18. BPC alignment. largest shift can observed retention time range 120 130s. Apart retention time range, little changes can observed. next evaluate impact alignment EICs selected internal standards. thus first extract ion chromatograms alignment. can now evaluate alignment effect test ions. plot EICs alignment isotope labeled cystine methionine.  Figure 19. EICs cystine methionine alignment. non-endogenous cystine ion already well aligned difference minimal. methionine ion, however, shows improvement alignment. addition visual inspection results, also evaluate impact alignment comparing variance retention times internal standards alignment. end, first need identify chromatographic peaks sample m/z retention time close expected values internal standard. use matchValues() function MetaboAnnotation package (Rainer et al. 2022) using MzRtParam method identify chromatographic peaks similar m/z (+/- 50 ppm) retention time (+/- 10 seconds) internal standard’s values. parameters mzColname rtColname specify column names query () target (chromatographic peaks) contain m/z retention time values match entities. perform matching separately sample. internal standard every sample, use filterMatches() function SingleMatchParam() parameter select chromatographic peak highest intensity. now internal standard ID chromatographic peak sample likely represents signal ion. can now extract retention times chromatographic peaks alignment. can now evaluate impact alignment retention times internal standards across full data set:  Figure 20. Retention time variation internal standards alignment. average, variation retention times internal standards across samples slightly reduced alignment. briefly touched subject correspondence determine anchor peaks alignment. Generally, goal correspondence analysis identify chromatographic peaks originate types ions samples experiment group LC-MS features. point, proper configuration parameter bw crucial. illustrate sensible choices parameter’s value can made. use plotChromPeakDensity() function simulate correspondence analysis default values PeakGroups extracted ion chromatograms two selected isotope labeled ions. plot shows EIC top panel, apex position chromatographic peaks different samples (y-axis), along retention time (x-axis) lower panel.  Figure 21. Initial correspondence analysis, Cystine.  Figure 22. Initial correspondence analysis, Methionine. Grouping peaks depends smoothness previousl mentionned density curve can configured parameter bw. seen , smoothness high properly group features. looking default parameters, can observe indeed, bw parameter set bw = 30, high modern UHPLC-MS setups. reduce value parameter 1.8 evaluate impact.  Figure 23. Correspondence analysis optimized parameters, Cystine.  Figure 24. Correspondence analysis optimized parameters, Methionine. can observe peaks now grouped accurately single feature test ion. important parameters optimized : binsize: data generated high resolution MS instrument, thus select low value paramete. ppm: TOF instruments, suggested use value ppm larger 0 accommodate higher measurement error instrument larger m/z values. minFraction: set minFraction = 0.75, hence defining features chromatographic peak identified least 75% samples one sample groups. sampleGroups: use information available sampleData’s \"phenotype\" column. correspondence analysis suggested evaluate results selected EICs. extract signal m/z similar isotope labeled methionine larger retention time range. Importantly, show actual correspondence results, set simulate = FALSE plotChromPeakDensity() function.  Figure 25. Correspondence analysis results, Methionine. hoped, signal two different ions now grouped separate features. Generally, correspondence results evaluated extracted chromatograms. Another interesting information look distribution features along retention time axis. results correspondence analysis now stored, along results preprocessing steps, within XcmsExperiment result object. correspondence results, .e., definition LC-MS features, can extracted using featureDefinitions() function. data frame provides average m/z retention time (columns \"mzmed\" \"rtmed\") characterize LC-MS feature. Column, \"peakidx\" contains indices chromatographic peaks assigned feature. actual abundances features, represent also final preprocessing results, can extracted featureValues() function: can note features (e.g. F0003 F0006) missing values samples. expected certain degree samples features, respectively ions, need present. address next section. previously observed missing values (NA) attributed various reasons. Although might represent genuinely missing value, indicating ion (feature) truly present particular sample, also result failure preceding chromatographic peak detection step. crucial able recover missing values latter category much possible reduce eventual need data imputation. next examine prevalent missing values present dataset: can observe substantial number missing values values dataset. Let’s therefore delve process gap-filling. first evaluate example features chromatographic peak detected samples:  Figure 26. Examples chromatographic peaks missing values. instances, chromatographic peak identified one two selected samples (red line), hence missing value reported feature particular samples (blue line). However, cases, signal measured samples, thus, reporting missing value correct example. signal feature low, likely reason peak detection failed. rescue signal cases, fillChromPeaks() function can used ChromPeakAreaParam approach. method defines m/z-retention time area feature based detected peaks, signal respective ion expected. integrates intensities within area samples missing values feature. reported feature abundance. apply method using default parameters. fillChromPeaks() thus rescue missing data data set. Note , even sample ion present, worst case noise integrated, expected much lower actual chromatographic peak signal. Let’s look previously missing values :  Figure 27. Examples chromatographic peaks missing values gap-filling. gap-filling, also blue colored sample chromatographic peak present peak area reported feature abundance sample. assess effectiveness gap-filling method rescuing signals, can also plot average signal features least one missing value average filled-signal. advisable perform analysis repeatedly measured samples; case, QC samples used. , extract: Feature values detected chromatographic peaks setting filled = FALSE featuresValues() call. filled-signal first extracting detected gap-filled abundances replace values detected chromatographic peaks NA. , calculate row averages matrices plot .  Figure 28. Detected vs. filled-signal. detected (x-axis) gap-filled (y-axis) values QC samples highly correlated. Especially higher abundances, agreement high, low intensities, can expected, differences higher trending correlation line. , addition, fit linear regression line data summarize results linear regression line slope 1.12 intercept -1.62. indicates filled-signal average 1.12 times higher detected signal. now restrict data set features chromatographic peak detected least 2/3 samples least one study samples groups. ensures statistical tests carried later study samples performed reliable signal. Also, filter remove features mostly detected QC samples, study samples. filter can performed filterFeatures() function xcms package PercentMissingFilter setting. parameters filer: threshold: defines maximal acceptable percentage samples missing value(s) least one sample groups defined parameter f. f: factor defining sample groups. replacing \"QC\" sample group NA parameter f exclude QC samples evaluation consider study samples. threshold = 40 keep features peak detected 2 3 samples one sample groups. consider detected chromatographic peaks per sample, apply filter \"raw\" assay result object, contains abundance values detected chromatographic peaks (prior gap-filling). final results LC-MS data preprocessing stored within XcmsExperiment object. includes identified chromatographic peaks, alignment results, well correspondence results. addition, guarantee reproducibility, result object keeps track performed processing steps, including individual parameter objects used configure . processHistory() function returns list various applied processing steps chronological order. , extract information first step performed preprocessing. processParam() function used extract actual parameter class used configure processing step. final result whole LC-MS data preprocessing two-dimensional matrix abundances -called LC-MS features samples. Note stage analysis features characterized m/z retention time don’t yet information metabolite feature represent. seen , feature matrix can extracted featureValues() function corresponding feature characteristics (.e., m/z retention time values) using featureDefinitions() function. Thus, two arrays extracted xcms result object used/imported analysis packages processing. example also exported tab delimited text files, used external tool, used, also MS2 spectra available, feature-based molecular networking GNPS analysis environment (Nothias et al. 2020). processing R, reference link raw MS data required, suggested extract xcms preprocessing result using quantify() function SummarizedExperiment object, Bioconductor’s default container data biological assays/experiments. simplifies integration Bioconductor analysis packages. quantify() function takes parameters featureValues() function, thus, call extract SummarizedExperiment detected, gap-filled, feature abundances: Sample identifications xcms result’s sampleData() now available colData() (column, sample annotations) featureDefinitions() rowData() (row, feature annotations). feature values added first assay() SummarizedExperiment even processing history available object’s metadata(). SummarizedExperiment supports multiple assays, numeric matrices dimensions. thus add detected gap-filled feature abundances additional assay SummarizedExperiment. Feature abundances can extracted assay() function. extract first 6 lines detected gap-filled feature abundances: advantage, addition container full preprocessing results also possibility easy intuitive creation data subsets ensuring data integrity. example easy subset full data selection features /samples: moving next step analysis, advisable save preprocessing results. multiple format options save , can found MsIO package documentation. save XcmsExperiment object file format handled alabster framework, ensures object can easily read languages like Python Javascript well loaded easily back R.","code":"#' Use default Centwave parameter param <- CentWaveParam()  #' Look at the default parameters param Object of class:  CentWaveParam  Parameters:  - ppm: [1] 25  - peakwidth: [1] 20 50  - snthresh: [1] 10  - prefilter: [1]   3 100  - mzCenterFun: [1] \"wMean\"  - integrate: [1] 1  - mzdiff: [1] -0.001  - fitgauss: [1] FALSE  - noise: [1] 0  - verboseColumns: [1] FALSE  - roiList: list()  - firstBaselineCheck: [1] TRUE  - roiScales: numeric(0)  - extendLengthMSW: [1] FALSE  - verboseBetaColumns: [1] FALSE #' Evaluate for Cystine cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt rtmin rtmax into intb maxo sn row column #' Evaluate for Methionine met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt rtmin rtmax into intb maxo sn row column #' Restrict the data to signal from cystine in the first sample cst <- lcms1[1L] |>   spectra() |>   filterRt(rt = c(208, 218)) |>   filterMzRange(mz = fData(eic_cystine)[\"cystine_13C_15N\", c(\"mzmin\", \"mzmax\")])  #' Show the number of peaks per m/z filtered spectra lengths(cst) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 #' Calculate the difference in m/z values between scans mz_diff <- cst |>     mz() |>     unlist() |>     diff() |>     abs()  #' Express differences in ppm range(mz_diff * 1e6 / mean(unlist(mz(cst)))) [1]  0.08829605 14.82188728 #' Parameters adapted for chromatographic peak detection on EICs. param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2,                        snthresh = 2)  #' Evaluate on the cystine ion cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt   rtmin   rtmax      into      intb      maxo sn row column  [1,] 209.251 207.577 212.878  4085.675  2911.376  2157.459  4   1      1  [2,] 209.251 206.182 213.995 24625.728 19074.407 12907.487  4   1      2  [3,] 209.252 207.020 214.274 19467.836 14594.041  9996.466  4   1      3  [4,] 209.251 207.577 212.041  4648.229  3202.617  2458.485  3   1      4  [5,] 208.974 206.184 213.159 23801.825 18126.978 11300.289  3   1      5  [6,] 209.250 207.018 213.714 25990.327 21036.768 13650.329  5   1      6  [7,] 209.252 207.857 212.879  4528.767  3259.039  2445.841  4   1      7  [8,] 209.252 207.299 213.995 23119.449 17274.140 12153.410  4   1      8  [9,] 208.972 206.740 212.878 28943.188 23436.119 14451.023  4   1      9 [10,] 209.252 207.578 213.437  4470.552  3065.402  2292.881  4   1     10 #' Evaluate on the methionine ion met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt   rtmin   rtmax     into     intb      maxo sn row column  [1,] 159.867 157.913 162.378 20026.61 14715.42 12555.601  4   1      1  [2,] 160.425 157.077 163.215 16827.76 11843.39  8407.699  3   1      2  [3,] 160.425 157.356 163.215 18262.45 12881.67  9283.375  3   1      3  [4,] 159.588 157.635 161.820 20987.72 15424.25 13327.811  4   1      4  [5,] 160.985 156.799 163.217 16601.72 11968.46 10012.396  4   1      5  [6,] 160.982 157.634 163.214 17243.24 12389.94  9150.079  4   1      6  [7,] 159.867 158.193 162.099 21120.10 16202.05 13531.844  3   1      7  [8,] 160.426 157.356 162.937 18937.40 13739.73 10336.000  3   1      8  [9,] 160.704 158.472 163.215 17882.21 12299.43  9395.548  3   1      9 [10,] 160.146 157.914 162.379 20275.80 14279.50 12669.821  3   1     10 #' Plot test chromatogram par(mfrow = c(1, 2)) plot(cystine_test, main = fData(cystine_test)$name,      col = paste0(col_sample, 80),      peakBg = paste0(col_sample, 40)[chromPeaks(cystine_test)[, \"column\"]],      cex.main = 0.8, cex.axis = 0.8) grid()  plot(met_test, main = fData(met_test)$name,      col = paste0(col_sample, 80),      peakBg = paste0(col_sample, 40)[chromPeaks(met_test)[, \"column\"]],      cex.main = 0.8, cex.axis = 0.8) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\") #' Using the same settings, but with default snthresh param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms1 <- findChromPeaks(lcms1, param = param, chunkSize = 5)  #' Update EIC internal standard object eics_is_noprocess <- eic_is eic_is <- chromatogram(lcms1,,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) Processing chromatographic peaks fData(eic_is) <- fData(eics_is_noprocess) #' Extract m/z-rt regions for selected peaks mz_rt <- cbind(rtmin = c(155.2120, 181.71800),                rtmax = c(201.0710, 228.13500),                mzmin = c(191.0288,  53.50964),                mzmax = c(191.0527, 53.53183))  #' Extract the EICs eics <- chromatogram(lcms1[3], rt = mz_rt[, c(\"rtmin\", \"rtmax\")],                      mz = mz_rt[, c(\"mzmin\", \"mzmax\")]) Processing chromatographic peaks #' Plot the EICs plot(eics) #' set up the parameter param <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75)  #' Perform the peak refinement on the EICs eics <- refineChromPeaks(eics, param = param) plot(eics) #' Apply on whole dataset lcms1 <- refineChromPeaks(lcms1, param = param, chunkSize = 5) Reduced from 106714 to 89182 chromatographic peaks. chromPeakData(lcms1)$merged |>                       table() FALSE  TRUE 79908  9274 eics_is_chrompeaks <- eic_is  eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_chrompeaks)  eic_cystine <- eic_is[\"cystine_13C_15N\", ] eic_met <- eic_is[\"methionine_13C_15N\", ] #' Count the number of peaks per sample and summarize them in a table. data.frame(sample_name = sampleData(lcms1)$sample_name,            peak_count = as.integer(table(chromPeaks(lcms1)[, \"sample\"]))) |>   t() |>   kable(format = \"pipe\") #' Get QC samples QC_samples <- sampleData(lcms1)$phenotype == \"QC\"  #' extract BPC lcms1[QC_samples] |>     chromatogram(aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(col = col_phenotype[\"QC\"], main = \"BPC of QC samples\") |>     grid() # Initial correspondence analysis param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype == \"QC\",                           minFraction = 0.9,                           binSize = 0.01, ppm = 10,                           bw = 2) lcms1 <- groupChromPeaks(lcms1, param = param)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) #' Define parameters of choice subset <- which(sampleData(lcms1)$phenotype == \"QC\") param <- PeakGroupsParam(minFraction = 0.9, extraPeaks = 50, span = 0.5,                          subsetAdjust = \"average\",                          subset = subset)  #' Perform the alignment lcms1 <- adjustRtime(lcms1, param = param) #' Visualize alignment results plotAdjustedRtime(lcms1, col = paste0(col_sample, 80), peakGroupsPch = 1) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\") #' Get data before alignment lcms1_raw <- dropAdjustedRtime(lcms1)  #' Apply the adjusted retention time to our dataset lcms1 <- applyAdjustedRtime(lcms1) #' Plot the BPC before and after alignment par(mfrow = c(2, 1), mar = c(2, 1, 1, 0.5)) chromatogram(lcms1_raw, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC before alignment\", col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE)  chromatogram(lcms1, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC after alignment\",          col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE) #' Update the EICs eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fdata  #' Extract the EICs for the test ions eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"] par(mfrow = c(2, 2), mar = c(4, 4.5, 2, 1))  old_eic_cystine <- eic_is[\"cystine_13C_15N\"] plot(old_eic_cystine, main = \"Cystine before alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  old_eic_met <- eic_is[\"methionine_13C_15N\"] plot(old_eic_met, main = \"Methionine before alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  plot(eic_cystine, main = \"Cystine after alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\")  plot(eic_met, main = \"Methionine after alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3) #' Extract the matrix with all chromatographic peaks and add a column #' with the ID of the chromatographic peak chrom_peaks <- chromPeaks(lcms1) |> as.data.frame() chrom_peaks$peak_id <- rownames(chrom_peaks)  #' Define the parameters for the matching and filtering of the matches p_1 <- MzRtParam(ppm = 50, toleranceRt = 10) p_2 <- SingleMatchParam(duplicates = \"top_ranked\", column = \"target_maxo\",                         decreasing = TRUE)  #' Iterate over samples and identify for each the chromatographic peaks #' with similar m/z and retention time than the onse from the internal #' standard, and extract among them the ID of the peaks with the #' highest intensity. intern_standard_peaks <- lapply(seq_along(lcms1), function(i) {     tmp <- chrom_peaks[chrom_peaks[, \"sample\"] == i, , drop = FALSE]     mtch <- matchValues(intern_standard, tmp,                         mzColname = c(\"mz\", \"mz\"),                         rtColname = c(\"RT\", \"rt\"),                         param = p_1)     mtch <- filterMatches(mtch, p_2)     mtch$target_peak_id }) |>     do.call(what = cbind) #' Define the index of the selected chromatographic peaks in the #' full chromPeaks matrix idx <- match(intern_standard_peaks, rownames(chromPeaks(lcms1)))  #' Extract the raw retention times for these rt_raw <- chromPeaks(lcms1_raw)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw))  #' Extract the adjusted retention times for these rt_adj <- chromPeaks(lcms1)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw)) list(all_raw = rowSds(rt_raw, na.rm = TRUE),      all_adj = rowSds(rt_adj, na.rm = TRUE)      ) |>     vioplot() grid() #' Default parameter for the grouping and apply them to the test ions BPC param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 30)  param Object of class:  PeakDensityParam  Parameters:  - sampleGroups:  [1] \"QC\"  \"CVD\" \"CTR\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  - bw: [1] 30  - minFraction: [1] 0.5  - minSamples: [1] 1  - binSize: [1] 0.25  - maxFeatures: [1] 50  - ppm: [1] 0 plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Updating parameters param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 1.8)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Define the settings for the param param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype,                           minFraction = 0.75, binSize = 0.01, ppm = 10,                           bw = 1.8)  #' Apply to whole data lcms1 <- groupChromPeaks(lcms1, param = param) #' Extract chromatogram for an m/z similar to the one of the labeled methionine chr_test <- chromatogram(lcms1,                          mz = as.matrix(intern_standard[\"methionine_13C_15N\",                                                         c(\"mzmin\", \"mzmax\")]),                          rt = c(145, 200),                          aggregationFun = \"max\") plotChromPeakDensity(     chr_test, simulate = FALSE,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(chr_test)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(chr_test)[, \"sample\"]], 20),     peakPch = 16) # Bin features per RT slices vc <- featureDefinitions(lcms1)$rtmed breaks <- seq(0, max(vc, na.rm = TRUE) + 1, length.out = 15) |>     round(0) cuts <- cut(vc, breaks = breaks, include.lowest = TRUE)  table(cuts) cuts    [0,17]   (17,34]   (34,51]   (51,68]   (68,86]  (86,103] (103,120] (120,137]        15      2608       649        24       132        11        15       105 (137,154] (154,171] (171,188] (188,205] (205,222] (222,240]      1441      2202       410       869       554        33 #' Definition of the features featureDefinitions(lcms1) |>   head() mzmed    mzmin    mzmax    rtmed    rtmin    rtmax npeaks CTR CVD QC FT0001 50.98979 50.98949 50.99038 203.6001 203.1181 204.2331      8   1   3  4 FT0002 51.05904 51.05880 51.05941 191.1675 190.8787 191.5050      9   2   3  4 FT0003 51.98657 51.98631 51.98699 203.1467 202.6406 203.6710      7   0   3  4 FT0004 53.02036 53.02009 53.02043 203.2343 202.5652 204.0901     10   3   3  4 FT0005 53.52080 53.52051 53.52102 203.1936 202.8490 204.0901     10   3   3  4 FT0006 54.01007 54.00988 54.01015 159.2816 158.8499 159.4484      6   1   3  2             peakidx ms_level FT0001 7702, 16....        1 FT0002 7176, 16....        1 FT0003 7680, 17....        1 FT0004 7763, 17....        1 FT0005 8353, 17....        1 FT0006 5800, 15....        1 #' Extract feature abundances featureValues(lcms1, method = \"sum\") |>     head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422            NA              481.7436 FT0002              710.8078      875.9192            NA              693.6997 FT0003              445.5711      613.4410            NA              497.8866 FT0004            16994.5260    24605.7340     19766.707            17808.0933 FT0005             3284.2664     4526.0531      3521.822             3379.8909 FT0006            10681.7476    10009.6602            NA            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001            NA      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003            NA      634.9370              449.0933            NA FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006            NA     7296.4262                    NA     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785                    NA #' Percentage of missing values sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 26.41597 ftidx <- which(is.na(rowSums(featureValues(lcms1)))) fts <- rownames(featureDefinitions(lcms1))[ftidx] farea <- featureArea(lcms1, features = fts[1:2])  chromatogram(lcms1[c(2, 3)],              rt = farea[, c(\"rtmin\", \"rtmax\")],              mz = farea[, c(\"mzmin\", \"mzmax\")]) |>     plot(col = c(\"red\", \"blue\"), lwd = 2) #' Fill in the missing values in the whole dataset lcms1 <- fillChromPeaks(lcms1, param = ChromPeakAreaParam(), chunkSize = 5)  #' Percentage of missing values after gap-filling sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 5.155492 #' Get only detected signal in QC samples vals_detect <- featureValues(lcms1, filled = FALSE)[, QC_samples]  #' Get detected and filled-in signal vals_filled <- featureValues(lcms1)[, QC_samples]  #' Replace detected signal with NA vals_filled[!is.na(vals_detect)] <- NA  #' Identify features with at least one filled peak has_filled <- is.na(rowSums(vals_detect))  #' Calculate row averages for features with missing values avg_detect <- rowMeans(vals_detect[has_filled, ], na.rm = TRUE) avg_filled <- rowMeans(vals_filled[has_filled, ], na.rm = TRUE)  #' Plot the values against each other (in log2 scale) plot(log2(avg_detect), log2(avg_filled),      xlim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      ylim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      pch = 21, bg = \"#00000020\", col = \"#00000080\") grid() abline(0, 1) #' fit a linear regression line to the data l <- lm(log2(avg_filled) ~ log2(avg_detect)) summary(l) Call: lm(formula = log2(avg_filled) ~ log2(avg_detect))  Residuals:     Min      1Q  Median      3Q     Max -6.8176 -0.3807  0.1725  0.5492  6.7504  Coefficients:                  Estimate Std. Error t value Pr(>|t|) (Intercept)      -1.62359    0.11545  -14.06   <2e-16 *** log2(avg_detect)  1.11763    0.01259   88.75   <2e-16 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  Residual standard error: 0.9366 on 2846 degrees of freedom   (846 observations deleted due to missingness) Multiple R-squared:  0.7346,    Adjusted R-squared:  0.7345 F-statistic:  7877 on 1 and 2846 DF,  p-value: < 2.2e-16 #' Limit features to those with at least two detected peaks in one study group. #' Setting the value for QC samples to NA excludes QC samples from the #' calculation. f <- sampleData(lcms1)$phenotype f[f == \"QC\"] <- NA f <- as.factor(f) lcms1 <- filterFeatures(lcms1, PercentMissingFilter(f = f, threshold = 40)) 344 features were removed #' Check first step of the process history processHistory(lcms1)[[1]] Object of class \"XProcessHistory\"  type: Peak detection  date: Wed Dec  4 10:43:03 2024  info:  fileIndex: 1,2,3,4,5,6,7,8,9,10  Parameter class: CentWaveParam  MS level(s) 1 #' Extract results as a SummarizedExperiment res <- quantify(lcms1, method = \"sum\", filled = FALSE) res class: SummarizedExperiment dim: 8724 10 metadata(7): '' '' ... '' '' assays(1): raw rownames(8724): FT0001 FT0002 ... FT9067 FT9068 rowData names(11): mzmed mzmin ... QC ms_level colnames(10): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML ... MS_F_POS.mzML   MS_QC_POOL_4_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index assays(res)$raw_filled <- featureValues(lcms1, method = \"sum\",                                         filled = TRUE )  #' Different assay in the SummarizedExperiment object assayNames(res) [1] \"raw\"        \"raw_filled\" assay(res, \"raw_filled\") |> head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422      411.3295              481.7436 FT0002              710.8078      875.9192      457.5920              693.6997 FT0003              445.5711      613.4410      277.5022              497.8866 FT0004            16994.5260    24605.7340    19766.7069            17808.0933 FT0005             3284.2664     4526.0531     3521.8221             3379.8909 FT0006            10681.7476    10009.6602     9599.9701            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001      314.7567      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003      425.3774      634.9370              449.0933      556.2544 FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006     4792.2390     7296.4262             2382.1788     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785             6911.5439 res[1:14, 3:8] class: SummarizedExperiment dim: 14 6 metadata(7): '' '' ... '' '' assays(2): raw raw_filled rownames(14): FT0001 FT0002 ... FT0013 FT0014 rowData names(11): mzmed mzmin ... QC ms_level colnames(6): MS_B_POS.mzML MS_QC_POOL_2_POS.mzML ...   MS_QC_POOL_3_POS.mzML MS_E_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index #' XcmsExperiment object:  saveMsObject(lcms1,              AlabasterParam(path = file.path(\"objects/preprocessed_lcms1\")))  #' Below we remove the processHistory of the res object to allow for export,  #' this need to be fixed later. metadata(res) <- list() saveObject(res, file.path(\"objects/preprocessed_res\"))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"chromatographic-peak-detection","dir":"Articles","previous_headings":"","what":"Chromatographic peak detection","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"initial preprocessing step involves detecting intensity peaks along retention time axis, called chromatographic peaks. achieve , employ findChromPeaks() function within xcms. function supports various algorithms peak detection, can selected configured respective parameter objects. preferred algorithm case, CentWave, utilizes continuous wavelet transformation (CWT)-based peak detection (Tautenhahn, Böttcher, Neumann 2008). method known effectiveness handling non-Gaussian shaped chromatographic peaks peaks varying retention time widths, commonly encountered HILIC separations. apply CentWave algorithm default settings extracted ion chromatogram cystine methionine ions evaluate results. CentWave highly performant algorithm, requires costumized dataset. implies parameters fine-tuned based user’s data. example serves clear motivation users familiarize various parameters need adapt data set. discuss main parameters can easily adjusted suit user’s dataset: peakwidth: Specifies minimal maximal expected width peaks retention time dimension. Highly dependent chromatographic settings used. ppm: maximal allowed difference mass peaks’ m/z values (parts-per-million) consecutive scans consider representing signal ion. integrate: parameter defines integration method. , primarily use integrate = 2 integrates also signal chromatographic peak’s tail considered accurate developers. determine peakwidth, recommend users refer previous EICs estimate range peak widths observe dataset. Ideally, examining multiple EICs goal. dataset, peak widths appear around 2 10 seconds. advise choosing range wide narrow peakwidth parameter can lead false positives negatives. determine ppm, deeper analysis dataset needed. clarified ppm depends instrument, users necessarily input vendor-advertised ppm. ’s determine accurately possible: following steps involve generating highly restricted MS area single mass peak per spectrum, representing cystine ion. m/z peaks extracted, absolute difference calculated finally expressed ppm. therefore, choose value close maximum within range parameter ppm, .e., 15 ppm. can now perform chromatographic peak detection adapted settings EICs. important note , properly estimate background noise, sufficient data points outside chromatographic peak need present. generally problem peak detection performed full LC-MS data set, peak detection EICs retention time range EIC needs sufficiently wide. function fails find peak EIC, initial troubleshooting step increase range. Additionally, signal--noise threshold snthresh reduced peak detection EICs, within small retention time range, enough signal present properly estimate background noise. Finally, case MS1 data points per peaks, setting CentWave’s advanced parameter extendLengthMSW TRUE can help peak detection. customized parameters, chromatographic peak detected sample. , use plot() function EICs visualize results.  Figure 11. Chromatographic peak detection EICs. can see peak seems ot detected sample ions. indicates custom settings seem thus suitable dataset. now proceed apply entire dataset, extracting EICs ions evaluate confirm chromatographic peak detection worked expected. Note: revert value parameter snthresh default, , mentioned , background noise estimation reliable performed full data set. Parameter chunkSize findChromPeaks() defines number data files loaded memory processed simultaneously. parameter thus allows fine-tune memory demand well performance chromatographic peak detection step. plot EICs two selected internal standards evaluate chromatographic peak detection results.  Figure 12. Chromatographic peak detection EICs processing. Peaks seem detected properly samples ions. indicates peak detection process entire dataset successful. identification chromatographic peaks using CentWave algorithm can sometimes result artifacts, overlapping split peaks. address issue, refineChromPeaks() function utilized, conjunction MergeNeighboringPeaksParam, aims merging split peaks. show examples CentWave peak detection artifacts. examples pre-selected illustrate necessity next step:  Figure 13. Examples CentWave peak detection artifacts. cases signal presumably single type ion split two separate chromatographic peaks (indicated vertical line). MergeNeigboringPeaksParam allows combine split peaks. parameters algorithm defined : expandMz: Suggested kept relatively small (0.0015) prevent merging isotopes. expandRt: Usually set approximately half size average retention time width used chromatographic peak detection (case, 2.5 seconds). minProp: Used determine whether candidates merged. Chromatographic peaks overlapping m/z ranges (expanded side expandMz) tail--head distance retention time dimension less 2 * expandRt, signal higher minProp apex intensity chromatographic peak lower intensity, merged. Values parameter small avoid merging closely co-eluting ions, isomers. test settings EICs split peaks.  Figure 14. Examples CentWave peak detection artifacts merging. can observe artificially split peaks appropriately merged. Therefore, next apply settings entire dataset. peak merging, column \"merged\" result object’s chromPeakData() data frame can used evaluate chromatographic peaks result represent signal merged, originally identified chromatographic peaks. proceeding next preprocessing step generally suggested evaluate results chromatographic peak detection EICs e.g. internal standards compounds/ions known present samples. update EICs objects internal standards results chromatographic peak detection refinement. Additionally, evaluating comparing number identified chromatographic peaks samples data set can help spotting potentially problematic samples. count number chromatographic peaks per sample show numbers table. Table 5. Samples number identified chromatographic peaks. similar number chromatographic peaks identified within various samples data set. Additional options evaluate results chromatographic peak detection can implemented using plotChromPeaks() function summarizing results using base R commands.","code":"#' Use default Centwave parameter param <- CentWaveParam()  #' Look at the default parameters param Object of class:  CentWaveParam  Parameters:  - ppm: [1] 25  - peakwidth: [1] 20 50  - snthresh: [1] 10  - prefilter: [1]   3 100  - mzCenterFun: [1] \"wMean\"  - integrate: [1] 1  - mzdiff: [1] -0.001  - fitgauss: [1] FALSE  - noise: [1] 0  - verboseColumns: [1] FALSE  - roiList: list()  - firstBaselineCheck: [1] TRUE  - roiScales: numeric(0)  - extendLengthMSW: [1] FALSE  - verboseBetaColumns: [1] FALSE #' Evaluate for Cystine cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt rtmin rtmax into intb maxo sn row column #' Evaluate for Methionine met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt rtmin rtmax into intb maxo sn row column #' Restrict the data to signal from cystine in the first sample cst <- lcms1[1L] |>   spectra() |>   filterRt(rt = c(208, 218)) |>   filterMzRange(mz = fData(eic_cystine)[\"cystine_13C_15N\", c(\"mzmin\", \"mzmax\")])  #' Show the number of peaks per m/z filtered spectra lengths(cst) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 #' Calculate the difference in m/z values between scans mz_diff <- cst |>     mz() |>     unlist() |>     diff() |>     abs()  #' Express differences in ppm range(mz_diff * 1e6 / mean(unlist(mz(cst)))) [1]  0.08829605 14.82188728 #' Parameters adapted for chromatographic peak detection on EICs. param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2,                        snthresh = 2)  #' Evaluate on the cystine ion cystine_test <- findChromPeaks(eic_cystine, param = param) chromPeaks(cystine_test) rt   rtmin   rtmax      into      intb      maxo sn row column  [1,] 209.251 207.577 212.878  4085.675  2911.376  2157.459  4   1      1  [2,] 209.251 206.182 213.995 24625.728 19074.407 12907.487  4   1      2  [3,] 209.252 207.020 214.274 19467.836 14594.041  9996.466  4   1      3  [4,] 209.251 207.577 212.041  4648.229  3202.617  2458.485  3   1      4  [5,] 208.974 206.184 213.159 23801.825 18126.978 11300.289  3   1      5  [6,] 209.250 207.018 213.714 25990.327 21036.768 13650.329  5   1      6  [7,] 209.252 207.857 212.879  4528.767  3259.039  2445.841  4   1      7  [8,] 209.252 207.299 213.995 23119.449 17274.140 12153.410  4   1      8  [9,] 208.972 206.740 212.878 28943.188 23436.119 14451.023  4   1      9 [10,] 209.252 207.578 213.437  4470.552  3065.402  2292.881  4   1     10 #' Evaluate on the methionine ion met_test <- findChromPeaks(eic_met, param = param) chromPeaks(met_test) rt   rtmin   rtmax     into     intb      maxo sn row column  [1,] 159.867 157.913 162.378 20026.61 14715.42 12555.601  4   1      1  [2,] 160.425 157.077 163.215 16827.76 11843.39  8407.699  3   1      2  [3,] 160.425 157.356 163.215 18262.45 12881.67  9283.375  3   1      3  [4,] 159.588 157.635 161.820 20987.72 15424.25 13327.811  4   1      4  [5,] 160.985 156.799 163.217 16601.72 11968.46 10012.396  4   1      5  [6,] 160.982 157.634 163.214 17243.24 12389.94  9150.079  4   1      6  [7,] 159.867 158.193 162.099 21120.10 16202.05 13531.844  3   1      7  [8,] 160.426 157.356 162.937 18937.40 13739.73 10336.000  3   1      8  [9,] 160.704 158.472 163.215 17882.21 12299.43  9395.548  3   1      9 [10,] 160.146 157.914 162.379 20275.80 14279.50 12669.821  3   1     10 #' Plot test chromatogram par(mfrow = c(1, 2)) plot(cystine_test, main = fData(cystine_test)$name,      col = paste0(col_sample, 80),      peakBg = paste0(col_sample, 40)[chromPeaks(cystine_test)[, \"column\"]],      cex.main = 0.8, cex.axis = 0.8) grid()  plot(met_test, main = fData(met_test)$name,      col = paste0(col_sample, 80),      peakBg = paste0(col_sample, 40)[chromPeaks(met_test)[, \"column\"]],      cex.main = 0.8, cex.axis = 0.8) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\") #' Using the same settings, but with default snthresh param <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms1 <- findChromPeaks(lcms1, param = param, chunkSize = 5)  #' Update EIC internal standard object eics_is_noprocess <- eic_is eic_is <- chromatogram(lcms1,,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) Processing chromatographic peaks fData(eic_is) <- fData(eics_is_noprocess) #' Extract m/z-rt regions for selected peaks mz_rt <- cbind(rtmin = c(155.2120, 181.71800),                rtmax = c(201.0710, 228.13500),                mzmin = c(191.0288,  53.50964),                mzmax = c(191.0527, 53.53183))  #' Extract the EICs eics <- chromatogram(lcms1[3], rt = mz_rt[, c(\"rtmin\", \"rtmax\")],                      mz = mz_rt[, c(\"mzmin\", \"mzmax\")]) Processing chromatographic peaks #' Plot the EICs plot(eics) #' set up the parameter param <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75)  #' Perform the peak refinement on the EICs eics <- refineChromPeaks(eics, param = param) plot(eics) #' Apply on whole dataset lcms1 <- refineChromPeaks(lcms1, param = param, chunkSize = 5) Reduced from 106714 to 89182 chromatographic peaks. chromPeakData(lcms1)$merged |>                       table() FALSE  TRUE 79908  9274 eics_is_chrompeaks <- eic_is  eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_chrompeaks)  eic_cystine <- eic_is[\"cystine_13C_15N\", ] eic_met <- eic_is[\"methionine_13C_15N\", ] #' Count the number of peaks per sample and summarize them in a table. data.frame(sample_name = sampleData(lcms1)$sample_name,            peak_count = as.integer(table(chromPeaks(lcms1)[, \"sample\"]))) |>   t() |>   kable(format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"refine-identified-chromatographic-peaks","dir":"Articles","previous_headings":"Data preprocessing","what":"Refine identified chromatographic peaks","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"identification chromatographic peaks using CentWave algorithm can sometimes result artifacts, overlapping split peaks. address issue, refineChromPeaks() function utilized, conjunction MergeNeighboringPeaksParam, aims merging split peaks. show examples CentWave peak detection artifacts. examples pre-selected illustrate necessity next step:  Figure 13. Examples CentWave peak detection artifacts. cases signal presumably single type ion split two separate chromatographic peaks (indicated vertical line). MergeNeigboringPeaksParam allows combine split peaks. parameters algorithm defined : expandMz: Suggested kept relatively small (0.0015) prevent merging isotopes. expandRt: Usually set approximately half size average retention time width used chromatographic peak detection (case, 2.5 seconds). minProp: Used determine whether candidates merged. Chromatographic peaks overlapping m/z ranges (expanded side expandMz) tail--head distance retention time dimension less 2 * expandRt, signal higher minProp apex intensity chromatographic peak lower intensity, merged. Values parameter small avoid merging closely co-eluting ions, isomers. test settings EICs split peaks.  Figure 14. Examples CentWave peak detection artifacts merging. can observe artificially split peaks appropriately merged. Therefore, next apply settings entire dataset. peak merging, column \"merged\" result object’s chromPeakData() data frame can used evaluate chromatographic peaks result represent signal merged, originally identified chromatographic peaks. proceeding next preprocessing step generally suggested evaluate results chromatographic peak detection EICs e.g. internal standards compounds/ions known present samples. update EICs objects internal standards results chromatographic peak detection refinement. Additionally, evaluating comparing number identified chromatographic peaks samples data set can help spotting potentially problematic samples. count number chromatographic peaks per sample show numbers table. Table 5. Samples number identified chromatographic peaks. similar number chromatographic peaks identified within various samples data set. Additional options evaluate results chromatographic peak detection can implemented using plotChromPeaks() function summarizing results using base R commands.","code":"#' Extract m/z-rt regions for selected peaks mz_rt <- cbind(rtmin = c(155.2120, 181.71800),                rtmax = c(201.0710, 228.13500),                mzmin = c(191.0288,  53.50964),                mzmax = c(191.0527, 53.53183))  #' Extract the EICs eics <- chromatogram(lcms1[3], rt = mz_rt[, c(\"rtmin\", \"rtmax\")],                      mz = mz_rt[, c(\"mzmin\", \"mzmax\")]) Processing chromatographic peaks #' Plot the EICs plot(eics) #' set up the parameter param <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75)  #' Perform the peak refinement on the EICs eics <- refineChromPeaks(eics, param = param) plot(eics) #' Apply on whole dataset lcms1 <- refineChromPeaks(lcms1, param = param, chunkSize = 5) Reduced from 106714 to 89182 chromatographic peaks. chromPeakData(lcms1)$merged |>                       table() FALSE  TRUE 79908  9274 eics_is_chrompeaks <- eic_is  eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fData(eics_is_chrompeaks)  eic_cystine <- eic_is[\"cystine_13C_15N\", ] eic_met <- eic_is[\"methionine_13C_15N\", ] #' Count the number of peaks per sample and summarize them in a table. data.frame(sample_name = sampleData(lcms1)$sample_name,            peak_count = as.integer(table(chromPeaks(lcms1)[, \"sample\"]))) |>   t() |>   kable(format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"retention-time-alignment","dir":"Articles","previous_headings":"","what":"Retention time alignment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Despite using chromatographic settings conditions retention time shifts unavoidable. Indeed, performance instrument can change time, example due small variations environmental conditions, temperature pressure. shifts generally small samples measured within batch/measurement run, can considerable data experiment acquired across longer time period. evaluate presence shift extract plot BPC QC samples.  Figure 15. BPC QC samples. QC samples representing sample (pool) measured regular intervals measurement run experiment measured day. Still, small shifts can observed, especially region 100 150 seconds. facilitate proper correspondence signals across samples (hence definition LC-MS features), essential minimize differences retention times. Theoretically, proceed two steps: first select QC samples dataset first alignment , using -called anchor peaks. way can assume linear shift time, since always measuring sample different regular time intervals. Despite external QCs data set, still use subset-based alignment assuming retention time shifts independent different sample matrix (human serum plasma) instead mostly instrument-dependent. Note also possible manually specify anchor peaks, respectively retention times align data set external, reference, data set. information provided vignettes xcms package. calculating much adjust retention time samples, apply shift also study samples. xcms retention time alignment can performed using adjustRtime() function alignment algorithm. example use PeakGroups method (Smith et al. 2006) performs alignment minimizing differences retention times set anchor peaks different samples. method requires initial correspondence analysis match/group chromatographic peaks across samples algorithm selects anchor peaks alignment. initial correspondence, use PeakDensity approach (Smith et al. 2006) groups chromatographic peaks similar m/z retention time LC-MS features. parameters algorithm, can configured using PeakDensityParam object, sampleGroups, minFraction, binSize, ppm bw. binSize, ppm bw allow specify similar chromatographic peaks’ m/z retention time values need consider grouping feature. binSize ppm define required similarity m/z values. Within m/z bin (defined binSize ppm) areas along retention time axis high chromatographic peak density (considering peaks samples) identified, chromatographic peaks within regions considered grouping feature. High density areas identified using base R density() function, bw parameter: higher values define wider retention time areas, lower values require chromatographic peaks similar retention times. parameter can seen black line plot , corresponding smoothness density curve. Whether candidate peaks get grouped feature depends also parameters sampleGroups minFraction: sampleGroups provide, sample, sample group belongs . minFraction expected value 0 1 defining proportion samples within least one sample groups (defined sampleGroups) chromatographic peaks detected group feature. initial correspondence, parameters don’t need fully optimized. Selection dataset-specific parameter values described detail next section. dataset, use small values binSize ppm , importantly, also parameter bw, since data set ultra high performance (UHP) LC setup used. minFraction use high value (0.9) ensure features defined chromatographic peaks present almost samples one sample group (can used anchor peaks actual alignment). base alignment later QC samples hence define sampleGroups binary variable grouping samples either study, QC group.  Figure 16. Initial correspondence analysis. PeakGroups-based alignment can next performed using adjustRtime() function PeakGroupsParam parameter object. parameters algorithm : subsetAdjust subset: Allows subset alignment. base retention time alignment QC samples, .e., retention time shifts estimated based repeatedly measured samples. resulting adjustment applied entire data. data sets QC samples (e.g. sample pools) measured repeatedly, strongly suggest use method. Note also subset-based alignment samples ordered injection index (.e., order measured measurement run). minFraction: value 0 1 defining proportion samples (full data set, data subset defined subset) chromatographic peak identified use anchor peak. contrast PeakDensityParam parameter used define proportion within sample group. span: PeakGroups method allows, depending data, adjust regions along retention time axis differently. enable local alignments LOESS function used parameter defines degree smoothing function. Generally, values 0.4 0.6 used, however, suggested evaluate alignment results eventually adapt parameters result satisfactory. perform alignment data set based retention times anchor peaks defined subset QC samples. Alignment adjusted retention times spectra data set, well retention times identified chromatographic peaks. alignment performed, user evaluate results using plotAdjustedRtime() function. function visualizes difference adjusted raw retention time sample y-axis along adjusted retention time x-axis. Dot points represent position used anchor peak along retention time axis. optimal alignment areas along retention time axis, anchor peaks scattered retention time dimension.  Figure 17. Retention time alignment results. samples present data set measured within measurement run, resulting small retention time shifts. Therefore, little adjustments needed performed (shifts maximum 1 second can seen plot ). Generally, magnitude adjustment seen plots match expectation analyst. can also compare BPC alignment. get original data, .e. raw retention times, can use dropAdjustedRtime() function:  Figure 18. BPC alignment. largest shift can observed retention time range 120 130s. Apart retention time range, little changes can observed. next evaluate impact alignment EICs selected internal standards. thus first extract ion chromatograms alignment. can now evaluate alignment effect test ions. plot EICs alignment isotope labeled cystine methionine.  Figure 19. EICs cystine methionine alignment. non-endogenous cystine ion already well aligned difference minimal. methionine ion, however, shows improvement alignment. addition visual inspection results, also evaluate impact alignment comparing variance retention times internal standards alignment. end, first need identify chromatographic peaks sample m/z retention time close expected values internal standard. use matchValues() function MetaboAnnotation package (Rainer et al. 2022) using MzRtParam method identify chromatographic peaks similar m/z (+/- 50 ppm) retention time (+/- 10 seconds) internal standard’s values. parameters mzColname rtColname specify column names query () target (chromatographic peaks) contain m/z retention time values match entities. perform matching separately sample. internal standard every sample, use filterMatches() function SingleMatchParam() parameter select chromatographic peak highest intensity. now internal standard ID chromatographic peak sample likely represents signal ion. can now extract retention times chromatographic peaks alignment. can now evaluate impact alignment retention times internal standards across full data set:  Figure 20. Retention time variation internal standards alignment. average, variation retention times internal standards across samples slightly reduced alignment.","code":"#' Get QC samples QC_samples <- sampleData(lcms1)$phenotype == \"QC\"  #' extract BPC lcms1[QC_samples] |>     chromatogram(aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(col = col_phenotype[\"QC\"], main = \"BPC of QC samples\") |>     grid() # Initial correspondence analysis param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype == \"QC\",                           minFraction = 0.9,                           binSize = 0.01, ppm = 10,                           bw = 2) lcms1 <- groupChromPeaks(lcms1, param = param)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) #' Define parameters of choice subset <- which(sampleData(lcms1)$phenotype == \"QC\") param <- PeakGroupsParam(minFraction = 0.9, extraPeaks = 50, span = 0.5,                          subsetAdjust = \"average\",                          subset = subset)  #' Perform the alignment lcms1 <- adjustRtime(lcms1, param = param) #' Visualize alignment results plotAdjustedRtime(lcms1, col = paste0(col_sample, 80), peakGroupsPch = 1) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\") #' Get data before alignment lcms1_raw <- dropAdjustedRtime(lcms1)  #' Apply the adjusted retention time to our dataset lcms1 <- applyAdjustedRtime(lcms1) #' Plot the BPC before and after alignment par(mfrow = c(2, 1), mar = c(2, 1, 1, 0.5)) chromatogram(lcms1_raw, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC before alignment\", col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE)  chromatogram(lcms1, aggregationFun = \"max\", chromPeaks = \"none\") |>     plot(main = \"BPC after alignment\",          col = paste0(col_sample, 80)) grid() legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\", horiz = TRUE) #' Update the EICs eic_is <- chromatogram(lcms1,                        rt = as.matrix(intern_standard[, c(\"rtmin\", \"rtmax\")]),                        mz = as.matrix(intern_standard[, c(\"mzmin\", \"mzmax\")])) fData(eic_is) <- fdata  #' Extract the EICs for the test ions eic_cystine <- eic_is[\"cystine_13C_15N\"] eic_met <- eic_is[\"methionine_13C_15N\"] par(mfrow = c(2, 2), mar = c(4, 4.5, 2, 1))  old_eic_cystine <- eic_is[\"cystine_13C_15N\"] plot(old_eic_cystine, main = \"Cystine before alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  old_eic_met <- eic_is[\"methionine_13C_15N\"] plot(old_eic_met, main = \"Methionine before alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3)  plot(eic_cystine, main = \"Cystine after alignment\", peakType = \"none\",      col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"cystine_13C_15N\", \"RT\"], col = \"red\", lty = 3) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty = 1, bty = \"n\")  plot(eic_met, main = \"Methionine after alignment\",      peakType = \"none\", col = paste0(col_sample, 80)) grid() abline(v = intern_standard[\"methionine_13C_15N\", \"RT\"], col = \"red\", lty = 3) #' Extract the matrix with all chromatographic peaks and add a column #' with the ID of the chromatographic peak chrom_peaks <- chromPeaks(lcms1) |> as.data.frame() chrom_peaks$peak_id <- rownames(chrom_peaks)  #' Define the parameters for the matching and filtering of the matches p_1 <- MzRtParam(ppm = 50, toleranceRt = 10) p_2 <- SingleMatchParam(duplicates = \"top_ranked\", column = \"target_maxo\",                         decreasing = TRUE)  #' Iterate over samples and identify for each the chromatographic peaks #' with similar m/z and retention time than the onse from the internal #' standard, and extract among them the ID of the peaks with the #' highest intensity. intern_standard_peaks <- lapply(seq_along(lcms1), function(i) {     tmp <- chrom_peaks[chrom_peaks[, \"sample\"] == i, , drop = FALSE]     mtch <- matchValues(intern_standard, tmp,                         mzColname = c(\"mz\", \"mz\"),                         rtColname = c(\"RT\", \"rt\"),                         param = p_1)     mtch <- filterMatches(mtch, p_2)     mtch$target_peak_id }) |>     do.call(what = cbind) #' Define the index of the selected chromatographic peaks in the #' full chromPeaks matrix idx <- match(intern_standard_peaks, rownames(chromPeaks(lcms1)))  #' Extract the raw retention times for these rt_raw <- chromPeaks(lcms1_raw)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw))  #' Extract the adjusted retention times for these rt_adj <- chromPeaks(lcms1)[idx, \"rt\"] |>     matrix(ncol = length(lcms1_raw)) list(all_raw = rowSds(rt_raw, na.rm = TRUE),      all_adj = rowSds(rt_adj, na.rm = TRUE)      ) |>     vioplot() grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"correspondence","dir":"Articles","previous_headings":"","what":"Correspondence","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"briefly touched subject correspondence determine anchor peaks alignment. Generally, goal correspondence analysis identify chromatographic peaks originate types ions samples experiment group LC-MS features. point, proper configuration parameter bw crucial. illustrate sensible choices parameter’s value can made. use plotChromPeakDensity() function simulate correspondence analysis default values PeakGroups extracted ion chromatograms two selected isotope labeled ions. plot shows EIC top panel, apex position chromatographic peaks different samples (y-axis), along retention time (x-axis) lower panel.  Figure 21. Initial correspondence analysis, Cystine.  Figure 22. Initial correspondence analysis, Methionine. Grouping peaks depends smoothness previousl mentionned density curve can configured parameter bw. seen , smoothness high properly group features. looking default parameters, can observe indeed, bw parameter set bw = 30, high modern UHPLC-MS setups. reduce value parameter 1.8 evaluate impact.  Figure 23. Correspondence analysis optimized parameters, Cystine.  Figure 24. Correspondence analysis optimized parameters, Methionine. can observe peaks now grouped accurately single feature test ion. important parameters optimized : binsize: data generated high resolution MS instrument, thus select low value paramete. ppm: TOF instruments, suggested use value ppm larger 0 accommodate higher measurement error instrument larger m/z values. minFraction: set minFraction = 0.75, hence defining features chromatographic peak identified least 75% samples one sample groups. sampleGroups: use information available sampleData’s \"phenotype\" column. correspondence analysis suggested evaluate results selected EICs. extract signal m/z similar isotope labeled methionine larger retention time range. Importantly, show actual correspondence results, set simulate = FALSE plotChromPeakDensity() function.  Figure 25. Correspondence analysis results, Methionine. hoped, signal two different ions now grouped separate features. Generally, correspondence results evaluated extracted chromatograms. Another interesting information look distribution features along retention time axis. results correspondence analysis now stored, along results preprocessing steps, within XcmsExperiment result object. correspondence results, .e., definition LC-MS features, can extracted using featureDefinitions() function. data frame provides average m/z retention time (columns \"mzmed\" \"rtmed\") characterize LC-MS feature. Column, \"peakidx\" contains indices chromatographic peaks assigned feature. actual abundances features, represent also final preprocessing results, can extracted featureValues() function: can note features (e.g. F0003 F0006) missing values samples. expected certain degree samples features, respectively ions, need present. address next section.","code":"#' Default parameter for the grouping and apply them to the test ions BPC param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 30)  param Object of class:  PeakDensityParam  Parameters:  - sampleGroups:  [1] \"QC\"  \"CVD\" \"CTR\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  \"CTR\" \"CVD\" \"QC\"  - bw: [1] 30  - minFraction: [1] 0.5  - minSamples: [1] 1  - binSize: [1] 0.25  - maxFeatures: [1] 50  - ppm: [1] 0 plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Updating parameters param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype, bw = 1.8)  plotChromPeakDensity(     eic_cystine, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_cystine)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_cystine)[, \"sample\"]], 20),     peakPch = 16) plotChromPeakDensity(eic_met, param = param,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(eic_met)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(eic_met)[, \"sample\"]], 20),     peakPch = 16) #' Define the settings for the param param <- PeakDensityParam(sampleGroups = sampleData(lcms1)$phenotype,                           minFraction = 0.75, binSize = 0.01, ppm = 10,                           bw = 1.8)  #' Apply to whole data lcms1 <- groupChromPeaks(lcms1, param = param) #' Extract chromatogram for an m/z similar to the one of the labeled methionine chr_test <- chromatogram(lcms1,                          mz = as.matrix(intern_standard[\"methionine_13C_15N\",                                                         c(\"mzmin\", \"mzmax\")]),                          rt = c(145, 200),                          aggregationFun = \"max\") plotChromPeakDensity(     chr_test, simulate = FALSE,     col = paste0(col_sample, \"80\"),     peakCol = col_sample[chromPeaks(chr_test)[, \"sample\"]],     peakBg = paste0(col_sample[chromPeaks(chr_test)[, \"sample\"]], 20),     peakPch = 16) # Bin features per RT slices vc <- featureDefinitions(lcms1)$rtmed breaks <- seq(0, max(vc, na.rm = TRUE) + 1, length.out = 15) |>     round(0) cuts <- cut(vc, breaks = breaks, include.lowest = TRUE)  table(cuts) cuts    [0,17]   (17,34]   (34,51]   (51,68]   (68,86]  (86,103] (103,120] (120,137]        15      2608       649        24       132        11        15       105 (137,154] (154,171] (171,188] (188,205] (205,222] (222,240]      1441      2202       410       869       554        33 #' Definition of the features featureDefinitions(lcms1) |>   head() mzmed    mzmin    mzmax    rtmed    rtmin    rtmax npeaks CTR CVD QC FT0001 50.98979 50.98949 50.99038 203.6001 203.1181 204.2331      8   1   3  4 FT0002 51.05904 51.05880 51.05941 191.1675 190.8787 191.5050      9   2   3  4 FT0003 51.98657 51.98631 51.98699 203.1467 202.6406 203.6710      7   0   3  4 FT0004 53.02036 53.02009 53.02043 203.2343 202.5652 204.0901     10   3   3  4 FT0005 53.52080 53.52051 53.52102 203.1936 202.8490 204.0901     10   3   3  4 FT0006 54.01007 54.00988 54.01015 159.2816 158.8499 159.4484      6   1   3  2             peakidx ms_level FT0001 7702, 16....        1 FT0002 7176, 16....        1 FT0003 7680, 17....        1 FT0004 7763, 17....        1 FT0005 8353, 17....        1 FT0006 5800, 15....        1 #' Extract feature abundances featureValues(lcms1, method = \"sum\") |>     head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422            NA              481.7436 FT0002              710.8078      875.9192            NA              693.6997 FT0003              445.5711      613.4410            NA              497.8866 FT0004            16994.5260    24605.7340     19766.707            17808.0933 FT0005             3284.2664     4526.0531      3521.822             3379.8909 FT0006            10681.7476    10009.6602            NA            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001            NA      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003            NA      634.9370              449.0933            NA FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006            NA     7296.4262                    NA     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785                    NA"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"gap-filling","dir":"Articles","previous_headings":"","what":"Gap filling","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"previously observed missing values (NA) attributed various reasons. Although might represent genuinely missing value, indicating ion (feature) truly present particular sample, also result failure preceding chromatographic peak detection step. crucial able recover missing values latter category much possible reduce eventual need data imputation. next examine prevalent missing values present dataset: can observe substantial number missing values values dataset. Let’s therefore delve process gap-filling. first evaluate example features chromatographic peak detected samples:  Figure 26. Examples chromatographic peaks missing values. instances, chromatographic peak identified one two selected samples (red line), hence missing value reported feature particular samples (blue line). However, cases, signal measured samples, thus, reporting missing value correct example. signal feature low, likely reason peak detection failed. rescue signal cases, fillChromPeaks() function can used ChromPeakAreaParam approach. method defines m/z-retention time area feature based detected peaks, signal respective ion expected. integrates intensities within area samples missing values feature. reported feature abundance. apply method using default parameters. fillChromPeaks() thus rescue missing data data set. Note , even sample ion present, worst case noise integrated, expected much lower actual chromatographic peak signal. Let’s look previously missing values :  Figure 27. Examples chromatographic peaks missing values gap-filling. gap-filling, also blue colored sample chromatographic peak present peak area reported feature abundance sample. assess effectiveness gap-filling method rescuing signals, can also plot average signal features least one missing value average filled-signal. advisable perform analysis repeatedly measured samples; case, QC samples used. , extract: Feature values detected chromatographic peaks setting filled = FALSE featuresValues() call. filled-signal first extracting detected gap-filled abundances replace values detected chromatographic peaks NA. , calculate row averages matrices plot .  Figure 28. Detected vs. filled-signal. detected (x-axis) gap-filled (y-axis) values QC samples highly correlated. Especially higher abundances, agreement high, low intensities, can expected, differences higher trending correlation line. , addition, fit linear regression line data summarize results linear regression line slope 1.12 intercept -1.62. indicates filled-signal average 1.12 times higher detected signal.","code":"#' Percentage of missing values sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 26.41597 ftidx <- which(is.na(rowSums(featureValues(lcms1)))) fts <- rownames(featureDefinitions(lcms1))[ftidx] farea <- featureArea(lcms1, features = fts[1:2])  chromatogram(lcms1[c(2, 3)],              rt = farea[, c(\"rtmin\", \"rtmax\")],              mz = farea[, c(\"mzmin\", \"mzmax\")]) |>     plot(col = c(\"red\", \"blue\"), lwd = 2) #' Fill in the missing values in the whole dataset lcms1 <- fillChromPeaks(lcms1, param = ChromPeakAreaParam(), chunkSize = 5)  #' Percentage of missing values after gap-filling sum(is.na(featureValues(lcms1))) /     length(featureValues(lcms1)) * 100 [1] 5.155492 #' Get only detected signal in QC samples vals_detect <- featureValues(lcms1, filled = FALSE)[, QC_samples]  #' Get detected and filled-in signal vals_filled <- featureValues(lcms1)[, QC_samples]  #' Replace detected signal with NA vals_filled[!is.na(vals_detect)] <- NA  #' Identify features with at least one filled peak has_filled <- is.na(rowSums(vals_detect))  #' Calculate row averages for features with missing values avg_detect <- rowMeans(vals_detect[has_filled, ], na.rm = TRUE) avg_filled <- rowMeans(vals_filled[has_filled, ], na.rm = TRUE)  #' Plot the values against each other (in log2 scale) plot(log2(avg_detect), log2(avg_filled),      xlim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      ylim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),      pch = 21, bg = \"#00000020\", col = \"#00000080\") grid() abline(0, 1) #' fit a linear regression line to the data l <- lm(log2(avg_filled) ~ log2(avg_detect)) summary(l) Call: lm(formula = log2(avg_filled) ~ log2(avg_detect))  Residuals:     Min      1Q  Median      3Q     Max -6.8176 -0.3807  0.1725  0.5492  6.7504  Coefficients:                  Estimate Std. Error t value Pr(>|t|) (Intercept)      -1.62359    0.11545  -14.06   <2e-16 *** log2(avg_detect)  1.11763    0.01259   88.75   <2e-16 *** --- Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1  Residual standard error: 0.9366 on 2846 degrees of freedom   (846 observations deleted due to missingness) Multiple R-squared:  0.7346,    Adjusted R-squared:  0.7345 F-statistic:  7877 on 1 and 2846 DF,  p-value: < 2.2e-16"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"filtering-features-missing-values","dir":"Articles","previous_headings":"","what":"Filtering Features: Missing values","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"now restrict data set features chromatographic peak detected least 2/3 samples least one study samples groups. ensures statistical tests carried later study samples performed reliable signal. Also, filter remove features mostly detected QC samples, study samples. filter can performed filterFeatures() function xcms package PercentMissingFilter setting. parameters filer: threshold: defines maximal acceptable percentage samples missing value(s) least one sample groups defined parameter f. f: factor defining sample groups. replacing \"QC\" sample group NA parameter f exclude QC samples evaluation consider study samples. threshold = 40 keep features peak detected 2 3 samples one sample groups. consider detected chromatographic peaks per sample, apply filter \"raw\" assay result object, contains abundance values detected chromatographic peaks (prior gap-filling).","code":"#' Limit features to those with at least two detected peaks in one study group. #' Setting the value for QC samples to NA excludes QC samples from the #' calculation. f <- sampleData(lcms1)$phenotype f[f == \"QC\"] <- NA f <- as.factor(f) lcms1 <- filterFeatures(lcms1, PercentMissingFilter(f = f, threshold = 40)) 344 features were removed"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"preprocessing-results","dir":"Articles","previous_headings":"","what":"Preprocessing results","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"final results LC-MS data preprocessing stored within XcmsExperiment object. includes identified chromatographic peaks, alignment results, well correspondence results. addition, guarantee reproducibility, result object keeps track performed processing steps, including individual parameter objects used configure . processHistory() function returns list various applied processing steps chronological order. , extract information first step performed preprocessing. processParam() function used extract actual parameter class used configure processing step. final result whole LC-MS data preprocessing two-dimensional matrix abundances -called LC-MS features samples. Note stage analysis features characterized m/z retention time don’t yet information metabolite feature represent. seen , feature matrix can extracted featureValues() function corresponding feature characteristics (.e., m/z retention time values) using featureDefinitions() function. Thus, two arrays extracted xcms result object used/imported analysis packages processing. example also exported tab delimited text files, used external tool, used, also MS2 spectra available, feature-based molecular networking GNPS analysis environment (Nothias et al. 2020). processing R, reference link raw MS data required, suggested extract xcms preprocessing result using quantify() function SummarizedExperiment object, Bioconductor’s default container data biological assays/experiments. simplifies integration Bioconductor analysis packages. quantify() function takes parameters featureValues() function, thus, call extract SummarizedExperiment detected, gap-filled, feature abundances: Sample identifications xcms result’s sampleData() now available colData() (column, sample annotations) featureDefinitions() rowData() (row, feature annotations). feature values added first assay() SummarizedExperiment even processing history available object’s metadata(). SummarizedExperiment supports multiple assays, numeric matrices dimensions. thus add detected gap-filled feature abundances additional assay SummarizedExperiment. Feature abundances can extracted assay() function. extract first 6 lines detected gap-filled feature abundances: advantage, addition container full preprocessing results also possibility easy intuitive creation data subsets ensuring data integrity. example easy subset full data selection features /samples: moving next step analysis, advisable save preprocessing results. multiple format options save , can found MsIO package documentation. save XcmsExperiment object file format handled alabster framework, ensures object can easily read languages like Python Javascript well loaded easily back R.","code":"#' Check first step of the process history processHistory(lcms1)[[1]] Object of class \"XProcessHistory\"  type: Peak detection  date: Wed Dec  4 10:43:03 2024  info:  fileIndex: 1,2,3,4,5,6,7,8,9,10  Parameter class: CentWaveParam  MS level(s) 1 #' Extract results as a SummarizedExperiment res <- quantify(lcms1, method = \"sum\", filled = FALSE) res class: SummarizedExperiment dim: 8724 10 metadata(7): '' '' ... '' '' assays(1): raw rownames(8724): FT0001 FT0002 ... FT9067 FT9068 rowData names(11): mzmed mzmin ... QC ms_level colnames(10): MS_QC_POOL_1_POS.mzML MS_A_POS.mzML ... MS_F_POS.mzML   MS_QC_POOL_4_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index assays(res)$raw_filled <- featureValues(lcms1, method = \"sum\",                                         filled = TRUE )  #' Different assay in the SummarizedExperiment object assayNames(res) [1] \"raw\"        \"raw_filled\" assay(res, \"raw_filled\") |> head() MS_QC_POOL_1_POS.mzML MS_A_POS.mzML MS_B_POS.mzML MS_QC_POOL_2_POS.mzML FT0001              421.6162      689.2422      411.3295              481.7436 FT0002              710.8078      875.9192      457.5920              693.6997 FT0003              445.5711      613.4410      277.5022              497.8866 FT0004            16994.5260    24605.7340    19766.7069            17808.0933 FT0005             3284.2664     4526.0531     3521.8221             3379.8909 FT0006            10681.7476    10009.6602     9599.9701            10800.5449        MS_C_POS.mzML MS_D_POS.mzML MS_QC_POOL_3_POS.mzML MS_E_POS.mzML FT0001      314.7567      635.2732              439.6086      570.5849 FT0002      781.2416      648.4344              700.9716     1054.0207 FT0003      425.3774      634.9370              449.0933      556.2544 FT0004    22780.6683    22873.1061            16965.7762    23432.1252 FT0005     4396.0762     4317.7734             3270.5290     4533.8667 FT0006     4792.2390     7296.4262             2382.1788     9236.9799        MS_F_POS.mzML MS_QC_POOL_4_POS.mzML FT0001      579.9360              437.0340 FT0002      534.4577              711.0361 FT0003      461.0465              232.1075 FT0004    22198.4607            16796.4497 FT0005     4161.0132             3142.2268 FT0006     6817.8785             6911.5439 res[1:14, 3:8] class: SummarizedExperiment dim: 14 6 metadata(7): '' '' ... '' '' assays(2): raw raw_filled rownames(14): FT0001 FT0002 ... FT0013 FT0014 rowData names(11): mzmed mzmin ... QC ms_level colnames(6): MS_B_POS.mzML MS_QC_POOL_2_POS.mzML ...   MS_QC_POOL_3_POS.mzML MS_E_POS.mzML colData names(11): sample_name derived_spectra_data_file ... phenotype   injection_index #' XcmsExperiment object:  saveMsObject(lcms1,              AlabasterParam(path = file.path(\"objects/preprocessed_lcms1\")))  #' Below we remove the processHistory of the res object to allow for export,  #' this need to be fixed later. metadata(res) <- list() saveObject(res, file.path(\"objects/preprocessed_res\"))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"data-normalization","dir":"Articles","previous_headings":"","what":"Data normalization","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"preprocessing, data normalization scaling might need applied remove technical variances data. simple approaches like median scaling can implemented lines R code, advanced normalization algorithms available packages Bioconductor’s preprocessCore. comprehensive workflow “Notame” also propose interesting normalization approach adaptable scalable user dataset (Klåvus et al. 2020). Generally, LC-MS data, bias can categorized three main groups(Broadhurst et al. 2018): Variances introduced sample collection initial processing, can include differences sample amounts. type bias expected sample-specific affect signals sample way. Methods like median scaling, LOESS quantiles normalization can adjust bias. Signal drifts along measurement samples experiment. Reasons drifts can related aging instrumentation used (columns, detector), also changes metabolite abundances characteristics due reactions modifications, oxidation. changes expected affect samples measured later run rather ones measured beginning. reason, bias can play major role large experiments bias can play major role large experiments measured long time range usually considered affect individual metabolites (metabolite groups) differently. adjustment, moving average linear regression-based approaches can used. latter can example performed using adjust_lm() function MetaboCoreUtils package. Batch-related biases. comprise noise specific larger set samples, can set samples measured one LC-MS measurement run (.e. one analysis plate) samples measured using specific batch reagents. noise assumed affect samples one batch way linear modeling-based approaches can used adjust . Unwanted variation can arise various sources highly dependent experiment. Therefore, data normalization chosen carefully based experimental design, statistical aims, balance accuracy precision achieved use auxiliary information. Sample preparation biases can evaluated using internal standards, depending however also added sample mixes sample processing. Repeated measurements QC samples hand allows estimate correct LC-MS specific biases. Also, proper planning experiment, measurement study samples random order, can largely avoid biases introduced mentioned sources variance. workflow present tools assess data quality evaluate need normalization well options normalization. space reasons able provide solutions adjust possible sources variation. principal component analysis (PCA) helpful tool initial, unsupervised, visualization data also provides insights potential quality issues data. order apply PCA measured feature abundances, need however impute (still present) missing values. assume missing values (gap-filling step) represent signal detection limit. cases, missing values can replaced random values sampled uniform distribution, ranging half smallest measured value smallest measured value specific feature. uniform distribution defined two parameters (minimum maximum) values equal probability selected. impute missing values approach add resulting data matrix new assay result object. PCA powerful tool detecting biases data. dimensionality reduction technique, enables visualization data lower-dimensional space. context LC-MS data, PCA can used identify overall biases batch, sample, injection index, etc. However, important note PCA linear method may able detect biases data. plotting PCA, apply log2 transform, center scale data. log2 transformation applied stabilize variance centering remove dependency absolute abundances.  Figure 28. PCA data coloured phenotypes. PCA shows clear separation study samples (plasma) QC samples (serum) first principal component (PC1). separation based phenotype visible third principal component (PC3). cases, can better option remove imputed values evaluate PCA . especially true imputed values replacing large proportion data. Global differences feature abundances samples (e.g. due sample-specific biases) can evaluated plotting distribution log2 transformed feature abundances using boxplots violin plots. show number detected chromatographic peaks per sample distribution log2 transformed feature abundances.  Figure 29. Number detected peaks feature abundances. upper part plot show gap filling steps allowed rescue substantial number NAs allowed us consistent number feature values per sample. consistency aligns asspumption every sample similar amount features detected. Additionally observe , average, signal distribution individual samples similar. alternative way evaluate differences abundances samples relative log abundance (RLA) plots (De Livera et al. 2012). RLA value abundance feature sample relative median abundance feature across multiple samples. can discriminate within group across group RLAs, depending whether abundance compared samples within sample group across samples. Within group RLA plots assess tightness replicates within groups median close zero low variation around . used across groups, allow compare behavior groups. Generally, -sample differences can easily spotted using RLA plots. calculate visualize within group RLA values using rowRla() function MsCoreUtils package defining parameter f sample groups.  Figure 30. RLA plot raw data filled data. RLA plot , can observe medians samples indeed centered around 0. Exception two CVD samples. Thus, distribution signals across samples comparable, differences seem present require sample normalization. previous RLA plot showed data biases need corrected. Therefore, implement -sample normalization using filled-features. process effectively mitigates variations influenced technical issues, differences sample preparation, processing injection methods. instance, employ commonly used technique known median scaling (De Livera et al. 2012). method involves computing median sample, followed determining median individual sample medians. ensures consistent median values sample throughout entire data set. Maintaining uniformity average total metabolite abundance across samples crucial effective implementation. process aims establish shared baseline central tendency metabolite abundance, mitigating impact sample-specific technical variations. approach fosters robust comparable analysis top features across data set. assumption normalizing based median, known lower sensitivity extreme values, enhances comparability top features ensures consistent average abundance across samples. median scaling calculated imputed non-imputed data, set stored separately within SummarizedExperiment object. approach facilitates testing various normalization strategies maintaining record processing steps undertaken, enabling easy regression previous stages necessary. crucial evaluate effectiveness normalization process. can achieved comparing distribution log2 transformed feature abundances normalization. Additionally, RLA plots can used assess tightness replicates within groups compare behavior groups.  Figure 31. PC1 PC2 data normalization. Normalization large impact PC1 PC2, separation study groups PC3 seems better difference QC samples lower normalization (see ).  Figure 32. PC3 PC4 data normalization. PCA plots show normalization process changed overall structure data. separation study QC samples remains . expected results normalization correct biological variance technical. compare RLA plots -sample normalization evaluate impact data.  Figure 33. RLA plot normalization. normalization process effectively centered data around median medians samples now closer zero. next evaluate coefficient variation (CV, also referred relative standard deviation RSD) features across samples either QC study samples. QC samples, CV represent technical noise, study samples include also expected biological differences. Thus, normalization reduce CV QC samples, slightly reducing CV study samples. CV calculated using rowRsd() function MetaboCoreUtils package. setting mad = TRUE use robust calculation using median absolute deviation instead standard deviation. Table 6. Distribution CV values across samples raw normalized data. table shows distribution CV raw normalized data. first column highlights % data given CV value, e.g. 25% data CV equal lower 0.04557 QC_raw data. anticipated, CV values QCs, reflect technical variance, lower compared study samples, include technical biological variance. Overall, minimal disparity exists raw normalized data, positive indication normalization process introduced bias dataset, also reflects little differences average abundances sample raw data. overall conclusion normalization process little variance present beginning, normalization however able center data around median (shown RLA plot). Given simplicity limited size example dataset, conclude normalization process stage. intricate datasets diverse biases, tailored approach devised. include also approaches adjust signal drifts batch effects. One possible option use linear-model based approach can example applied adjust_lm() function MetaboCoreUtils package.","code":"#' Impute missing values using an uniform distribution na_unidis <- function(z) {     na <- is.na(z)     if (any(na)) {         min = min(z, na.rm = TRUE)         z[na] <- runif(sum(na), min = min/2, max = min)     }     z }  #' Row-wise impute missing values and add the data as a new assay tmp <- apply(assay(res, \"raw_filled\"), MARGIN = 1, na_unidis) assays(res)$raw_filled_imputed <- t(tmp) #' Log2 transform and scale data vals <- assay(res, \"raw_filled_imputed\") |>     log2() |>     t() |>     scale(center = TRUE, scale = TRUE)  #' Perform the PCA pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  #' Plot the results vals_st <- cbind(vals, phenotype = res$phenotype) pca_12 <- autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) pca_34 <- autoplot(pca_res, data = vals_st, colour = 'phenotype',                    x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) grid.arrange(pca_12, pca_34, ncol = 1) layout(mat = matrix(1:3, ncol = 1), height = c(0.2, 0.2, 0.8))  par(mar = c(0.2, 4.5, 0.2, 3)) barplot(apply(assay(res, \"raw\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected peaks\", xaxt = \"n\", space = 0.012) grid(nx = NA, ny = NULL) barplot(apply(assay(res, \"raw_filled\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected + filled peaks\", xaxt = \"n\",         space = 0.012) grid(nx = NA, ny = NULL) vioplot(log2(assay(res, \"raw_filled\")), xaxt = \"n\",         ylab = expression(log[2]~feature~abundance),         col = paste0(col_sample, 80), border = col_sample) points(colMedians(log2(assay(res, \"raw_filled\")), na.rm = TRUE), type = \"b\",        pch = 1) grid(nx = NA, ny = NULL) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") par(mfrow = c(1, 1), mar = c(3.5, 4.5, 2.5, 1)) boxplot(MsCoreUtils::rowRla(assay(res, \"raw_filled\"),                             f = res$phenotype, transform = \"log2\"),         cex = 0.5, pch = 16,         col = paste0(col_sample, 80), ylab = \"RLA\",         border = col_sample, boxwex = 1,         outline = FALSE, xaxt = \"n\", main = \"Relative log abundance\",         cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = colData(res)$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty=3, lwd = 1, col = \"black\") legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") #' Compute median and generate normalization factor mdns <- apply(assay(res, \"raw_filled\"), MARGIN = 2,               median, na.rm = TRUE ) nf_mdn <- mdns / median(mdns)  #' divide dataset by median of median and create a new assay. assays(res)$norm <- sweep(assay(res, \"raw_filled\"), MARGIN = 2, nf_mdn, '/') assays(res)$norm_imputed <- sweep(assay(res, \"raw_filled_imputed\"), MARGIN = 2,                                   nf_mdn, '/') #' Data before normalization vals_st <- cbind(vals, phenotype = res$phenotype) pca_raw <- autoplot(pca_res, data = vals_st,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  #' Data after normalization vals_norm <- apply(assay(res, \"norm\"), MARGIN = 1, na_unidis) |>     log2() |>     scale(center = TRUE, scale = TRUE)  pca_res_norm <- prcomp(vals_norm, scale = FALSE, center = FALSE) vals_st_norm <- cbind(vals_norm, phenotype = res$phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) pca_raw <- autoplot(pca_res, data = vals_st ,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) par(mfrow = c(2, 1), mar = c(3.5, 4.5, 2.5, 1))  boxplot(rowRla(assay(res, \"raw_filled\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), cex.main = 1, outline = FALSE,         xaxt = \"n\", main = \"Raw data\", boxwex = 1) grid(nx = NA, ny = NULL) legend(\"topright\", inset = c(0, -0.2), col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE,        ncol = 3, cex = 0.7, bty = \"n\") abline(h = 0, lty=3, lwd = 1, col = \"black\")  boxplot(rowRla(assay(res, \"norm\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), boxwex = 1, outline = FALSE,         xaxt = \"n\", main = \"Normallized data\", cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = res$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty = 3, lwd = 1, col = \"black\") #' Calculate the CV values index_study <- res$phenotype %in% c(\"CTR\", \"CVD\") index_QC <- res$phenotype == \"QC\"  sample_res <- cbind(     QC_raw = rowRsd(assay(res, \"raw_filled\")[, index_QC],                     na.rm = TRUE, mad = TRUE),     QC_norm = rowRsd(assay(res, \"norm\")[, index_QC],                      na.rm = TRUE, mad = TRUE),     Study_raw = rowRsd(assay(res, \"raw_filled\")[, index_study],                        na.rm = TRUE, mad = TRUE),     Study_norm = rowRsd(assay(res, \"norm\")[, index_study],                         na.rm = TRUE, mad = TRUE) )  #' Summarize the values across features res_df <- data.frame(     QC_raw = quantile(sample_res[, \"QC_raw\"], na.rm = TRUE),     QC_norm = quantile(sample_res[, \"QC_norm\"], na.rm = TRUE),     Study_raw = quantile(sample_res[, \"Study_raw\"], na.rm = TRUE),     Study_norm = quantile(sample_res[, \"Study_norm\"], na.rm = TRUE) )  kable(res_df, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"initial-quality-assessment","dir":"Articles","previous_headings":"","what":"Initial quality assessment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"principal component analysis (PCA) helpful tool initial, unsupervised, visualization data also provides insights potential quality issues data. order apply PCA measured feature abundances, need however impute (still present) missing values. assume missing values (gap-filling step) represent signal detection limit. cases, missing values can replaced random values sampled uniform distribution, ranging half smallest measured value smallest measured value specific feature. uniform distribution defined two parameters (minimum maximum) values equal probability selected. impute missing values approach add resulting data matrix new assay result object. PCA powerful tool detecting biases data. dimensionality reduction technique, enables visualization data lower-dimensional space. context LC-MS data, PCA can used identify overall biases batch, sample, injection index, etc. However, important note PCA linear method may able detect biases data. plotting PCA, apply log2 transform, center scale data. log2 transformation applied stabilize variance centering remove dependency absolute abundances.  Figure 28. PCA data coloured phenotypes. PCA shows clear separation study samples (plasma) QC samples (serum) first principal component (PC1). separation based phenotype visible third principal component (PC3). cases, can better option remove imputed values evaluate PCA . especially true imputed values replacing large proportion data. Global differences feature abundances samples (e.g. due sample-specific biases) can evaluated plotting distribution log2 transformed feature abundances using boxplots violin plots. show number detected chromatographic peaks per sample distribution log2 transformed feature abundances.  Figure 29. Number detected peaks feature abundances. upper part plot show gap filling steps allowed rescue substantial number NAs allowed us consistent number feature values per sample. consistency aligns asspumption every sample similar amount features detected. Additionally observe , average, signal distribution individual samples similar. alternative way evaluate differences abundances samples relative log abundance (RLA) plots (De Livera et al. 2012). RLA value abundance feature sample relative median abundance feature across multiple samples. can discriminate within group across group RLAs, depending whether abundance compared samples within sample group across samples. Within group RLA plots assess tightness replicates within groups median close zero low variation around . used across groups, allow compare behavior groups. Generally, -sample differences can easily spotted using RLA plots. calculate visualize within group RLA values using rowRla() function MsCoreUtils package defining parameter f sample groups.  Figure 30. RLA plot raw data filled data. RLA plot , can observe medians samples indeed centered around 0. Exception two CVD samples. Thus, distribution signals across samples comparable, differences seem present require sample normalization.","code":"#' Impute missing values using an uniform distribution na_unidis <- function(z) {     na <- is.na(z)     if (any(na)) {         min = min(z, na.rm = TRUE)         z[na] <- runif(sum(na), min = min/2, max = min)     }     z }  #' Row-wise impute missing values and add the data as a new assay tmp <- apply(assay(res, \"raw_filled\"), MARGIN = 1, na_unidis) assays(res)$raw_filled_imputed <- t(tmp) #' Log2 transform and scale data vals <- assay(res, \"raw_filled_imputed\") |>     log2() |>     t() |>     scale(center = TRUE, scale = TRUE)  #' Perform the PCA pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  #' Plot the results vals_st <- cbind(vals, phenotype = res$phenotype) pca_12 <- autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) pca_34 <- autoplot(pca_res, data = vals_st, colour = 'phenotype',                    x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) grid.arrange(pca_12, pca_34, ncol = 1) layout(mat = matrix(1:3, ncol = 1), height = c(0.2, 0.2, 0.8))  par(mar = c(0.2, 4.5, 0.2, 3)) barplot(apply(assay(res, \"raw\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected peaks\", xaxt = \"n\", space = 0.012) grid(nx = NA, ny = NULL) barplot(apply(assay(res, \"raw_filled\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected + filled peaks\", xaxt = \"n\",         space = 0.012) grid(nx = NA, ny = NULL) vioplot(log2(assay(res, \"raw_filled\")), xaxt = \"n\",         ylab = expression(log[2]~feature~abundance),         col = paste0(col_sample, 80), border = col_sample) points(colMedians(log2(assay(res, \"raw_filled\")), na.rm = TRUE), type = \"b\",        pch = 1) grid(nx = NA, ny = NULL) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") par(mfrow = c(1, 1), mar = c(3.5, 4.5, 2.5, 1)) boxplot(MsCoreUtils::rowRla(assay(res, \"raw_filled\"),                             f = res$phenotype, transform = \"log2\"),         cex = 0.5, pch = 16,         col = paste0(col_sample, 80), ylab = \"RLA\",         border = col_sample, boxwex = 1,         outline = FALSE, xaxt = \"n\", main = \"Relative log abundance\",         cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = colData(res)$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty=3, lwd = 1, col = \"black\") legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"principal-component-analysis","dir":"Articles","previous_headings":"Data normalization","what":"Principal Component Analysis","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"PCA powerful tool detecting biases data. dimensionality reduction technique, enables visualization data lower-dimensional space. context LC-MS data, PCA can used identify overall biases batch, sample, injection index, etc. However, important note PCA linear method may able detect biases data. plotting PCA, apply log2 transform, center scale data. log2 transformation applied stabilize variance centering remove dependency absolute abundances.  Figure 28. PCA data coloured phenotypes. PCA shows clear separation study samples (plasma) QC samples (serum) first principal component (PC1). separation based phenotype visible third principal component (PC3). cases, can better option remove imputed values evaluate PCA . especially true imputed values replacing large proportion data.","code":"#' Log2 transform and scale data vals <- assay(res, \"raw_filled_imputed\") |>     log2() |>     t() |>     scale(center = TRUE, scale = TRUE)  #' Perform the PCA pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  #' Plot the results vals_st <- cbind(vals, phenotype = res$phenotype) pca_12 <- autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) pca_34 <- autoplot(pca_res, data = vals_st, colour = 'phenotype',                    x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) grid.arrange(pca_12, pca_34, ncol = 1)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"intensity-evaluation","dir":"Articles","previous_headings":"Data normalization","what":"Intensity evaluation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Global differences feature abundances samples (e.g. due sample-specific biases) can evaluated plotting distribution log2 transformed feature abundances using boxplots violin plots. show number detected chromatographic peaks per sample distribution log2 transformed feature abundances.  Figure 29. Number detected peaks feature abundances. upper part plot show gap filling steps allowed rescue substantial number NAs allowed us consistent number feature values per sample. consistency aligns asspumption every sample similar amount features detected. Additionally observe , average, signal distribution individual samples similar. alternative way evaluate differences abundances samples relative log abundance (RLA) plots (De Livera et al. 2012). RLA value abundance feature sample relative median abundance feature across multiple samples. can discriminate within group across group RLAs, depending whether abundance compared samples within sample group across samples. Within group RLA plots assess tightness replicates within groups median close zero low variation around . used across groups, allow compare behavior groups. Generally, -sample differences can easily spotted using RLA plots. calculate visualize within group RLA values using rowRla() function MsCoreUtils package defining parameter f sample groups.  Figure 30. RLA plot raw data filled data. RLA plot , can observe medians samples indeed centered around 0. Exception two CVD samples. Thus, distribution signals across samples comparable, differences seem present require sample normalization.","code":"layout(mat = matrix(1:3, ncol = 1), height = c(0.2, 0.2, 0.8))  par(mar = c(0.2, 4.5, 0.2, 3)) barplot(apply(assay(res, \"raw\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected peaks\", xaxt = \"n\", space = 0.012) grid(nx = NA, ny = NULL) barplot(apply(assay(res, \"raw_filled\"), MARGIN = 2, function(x) sum(!is.na(x))),         col = paste0(col_sample, 80), border = col_sample,         ylab = \"# detected + filled peaks\", xaxt = \"n\",         space = 0.012) grid(nx = NA, ny = NULL) vioplot(log2(assay(res, \"raw_filled\")), xaxt = \"n\",         ylab = expression(log[2]~feature~abundance),         col = paste0(col_sample, 80), border = col_sample) points(colMedians(log2(assay(res, \"raw_filled\")), na.rm = TRUE), type = \"b\",        pch = 1) grid(nx = NA, ny = NULL) legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\") par(mfrow = c(1, 1), mar = c(3.5, 4.5, 2.5, 1)) boxplot(MsCoreUtils::rowRla(assay(res, \"raw_filled\"),                             f = res$phenotype, transform = \"log2\"),         cex = 0.5, pch = 16,         col = paste0(col_sample, 80), ylab = \"RLA\",         border = col_sample, boxwex = 1,         outline = FALSE, xaxt = \"n\", main = \"Relative log abundance\",         cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = colData(res)$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty=3, lwd = 1, col = \"black\") legend(\"topright\", col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE, ncol = 3,        cex = 0.8,  bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"between-sample-normalisation","dir":"Articles","previous_headings":"","what":"Between sample normalisation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"previous RLA plot showed data biases need corrected. Therefore, implement -sample normalization using filled-features. process effectively mitigates variations influenced technical issues, differences sample preparation, processing injection methods. instance, employ commonly used technique known median scaling (De Livera et al. 2012). method involves computing median sample, followed determining median individual sample medians. ensures consistent median values sample throughout entire data set. Maintaining uniformity average total metabolite abundance across samples crucial effective implementation. process aims establish shared baseline central tendency metabolite abundance, mitigating impact sample-specific technical variations. approach fosters robust comparable analysis top features across data set. assumption normalizing based median, known lower sensitivity extreme values, enhances comparability top features ensures consistent average abundance across samples. median scaling calculated imputed non-imputed data, set stored separately within SummarizedExperiment object. approach facilitates testing various normalization strategies maintaining record processing steps undertaken, enabling easy regression previous stages necessary.","code":"#' Compute median and generate normalization factor mdns <- apply(assay(res, \"raw_filled\"), MARGIN = 2,               median, na.rm = TRUE ) nf_mdn <- mdns / median(mdns)  #' divide dataset by median of median and create a new assay. assays(res)$norm <- sweep(assay(res, \"raw_filled\"), MARGIN = 2, nf_mdn, '/') assays(res)$norm_imputed <- sweep(assay(res, \"raw_filled_imputed\"), MARGIN = 2,                                   nf_mdn, '/')"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"median-scaling","dir":"Articles","previous_headings":"Data normalization","what":"Median scaling","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"method involves computing median sample, followed determining median individual sample medians. ensures consistent median values sample throughout entire data set. Maintaining uniformity average total metabolite abundance across samples crucial effective implementation. process aims establish shared baseline central tendency metabolite abundance, mitigating impact sample-specific technical variations. approach fosters robust comparable analysis top features across data set. assumption normalizing based median, known lower sensitivity extreme values, enhances comparability top features ensures consistent average abundance across samples. median scaling calculated imputed non-imputed data, set stored separately within SummarizedExperiment object. approach facilitates testing various normalization strategies maintaining record processing steps undertaken, enabling easy regression previous stages necessary.","code":"#' Compute median and generate normalization factor mdns <- apply(assay(res, \"raw_filled\"), MARGIN = 2,               median, na.rm = TRUE ) nf_mdn <- mdns / median(mdns)  #' divide dataset by median of median and create a new assay. assays(res)$norm <- sweep(assay(res, \"raw_filled\"), MARGIN = 2, nf_mdn, '/') assays(res)$norm_imputed <- sweep(assay(res, \"raw_filled_imputed\"), MARGIN = 2,                                   nf_mdn, '/')"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"assessing-overall-effectiveness-of-the-normalization-approach","dir":"Articles","previous_headings":"","what":"Assessing overall effectiveness of the normalization approach","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"crucial evaluate effectiveness normalization process. can achieved comparing distribution log2 transformed feature abundances normalization. Additionally, RLA plots can used assess tightness replicates within groups compare behavior groups.  Figure 31. PC1 PC2 data normalization. Normalization large impact PC1 PC2, separation study groups PC3 seems better difference QC samples lower normalization (see ).  Figure 32. PC3 PC4 data normalization. PCA plots show normalization process changed overall structure data. separation study QC samples remains . expected results normalization correct biological variance technical. compare RLA plots -sample normalization evaluate impact data.  Figure 33. RLA plot normalization. normalization process effectively centered data around median medians samples now closer zero. next evaluate coefficient variation (CV, also referred relative standard deviation RSD) features across samples either QC study samples. QC samples, CV represent technical noise, study samples include also expected biological differences. Thus, normalization reduce CV QC samples, slightly reducing CV study samples. CV calculated using rowRsd() function MetaboCoreUtils package. setting mad = TRUE use robust calculation using median absolute deviation instead standard deviation. Table 6. Distribution CV values across samples raw normalized data. table shows distribution CV raw normalized data. first column highlights % data given CV value, e.g. 25% data CV equal lower 0.04557 QC_raw data. anticipated, CV values QCs, reflect technical variance, lower compared study samples, include technical biological variance. Overall, minimal disparity exists raw normalized data, positive indication normalization process introduced bias dataset, also reflects little differences average abundances sample raw data. overall conclusion normalization process little variance present beginning, normalization however able center data around median (shown RLA plot). Given simplicity limited size example dataset, conclude normalization process stage. intricate datasets diverse biases, tailored approach devised. include also approaches adjust signal drifts batch effects. One possible option use linear-model based approach can example applied adjust_lm() function MetaboCoreUtils package.","code":"#' Data before normalization vals_st <- cbind(vals, phenotype = res$phenotype) pca_raw <- autoplot(pca_res, data = vals_st,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  #' Data after normalization vals_norm <- apply(assay(res, \"norm\"), MARGIN = 1, na_unidis) |>     log2() |>     scale(center = TRUE, scale = TRUE)  pca_res_norm <- prcomp(vals_norm, scale = FALSE, center = FALSE) vals_st_norm <- cbind(vals_norm, phenotype = res$phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) pca_raw <- autoplot(pca_res, data = vals_st ,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) par(mfrow = c(2, 1), mar = c(3.5, 4.5, 2.5, 1))  boxplot(rowRla(assay(res, \"raw_filled\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), cex.main = 1, outline = FALSE,         xaxt = \"n\", main = \"Raw data\", boxwex = 1) grid(nx = NA, ny = NULL) legend(\"topright\", inset = c(0, -0.2), col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE,        ncol = 3, cex = 0.7, bty = \"n\") abline(h = 0, lty=3, lwd = 1, col = \"black\")  boxplot(rowRla(assay(res, \"norm\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), boxwex = 1, outline = FALSE,         xaxt = \"n\", main = \"Normallized data\", cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = res$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty = 3, lwd = 1, col = \"black\") #' Calculate the CV values index_study <- res$phenotype %in% c(\"CTR\", \"CVD\") index_QC <- res$phenotype == \"QC\"  sample_res <- cbind(     QC_raw = rowRsd(assay(res, \"raw_filled\")[, index_QC],                     na.rm = TRUE, mad = TRUE),     QC_norm = rowRsd(assay(res, \"norm\")[, index_QC],                      na.rm = TRUE, mad = TRUE),     Study_raw = rowRsd(assay(res, \"raw_filled\")[, index_study],                        na.rm = TRUE, mad = TRUE),     Study_norm = rowRsd(assay(res, \"norm\")[, index_study],                         na.rm = TRUE, mad = TRUE) )  #' Summarize the values across features res_df <- data.frame(     QC_raw = quantile(sample_res[, \"QC_raw\"], na.rm = TRUE),     QC_norm = quantile(sample_res[, \"QC_norm\"], na.rm = TRUE),     Study_raw = quantile(sample_res[, \"Study_raw\"], na.rm = TRUE),     Study_norm = quantile(sample_res[, \"Study_norm\"], na.rm = TRUE) )  kable(res_df, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"principal-component-analysis-1","dir":"Articles","previous_headings":"Data normalization","what":"Principal Component Analysis","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Figure 31. PC1 PC2 data normalization. Normalization large impact PC1 PC2, separation study groups PC3 seems better difference QC samples lower normalization (see ).  Figure 32. PC3 PC4 data normalization. PCA plots show normalization process changed overall structure data. separation study QC samples remains . expected results normalization correct biological variance technical.","code":"#' Data before normalization vals_st <- cbind(vals, phenotype = res$phenotype) pca_raw <- autoplot(pca_res, data = vals_st,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  #' Data after normalization vals_norm <- apply(assay(res, \"norm\"), MARGIN = 1, na_unidis) |>     log2() |>     scale(center = TRUE, scale = TRUE)  pca_res_norm <- prcomp(vals_norm, scale = FALSE, center = FALSE) vals_st_norm <- cbind(vals_norm, phenotype = res$phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1) pca_raw <- autoplot(pca_res, data = vals_st ,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype) pca_adj <- autoplot(pca_res_norm, data = vals_st_norm,                     colour = 'phenotype', x = 3, y = 4, scale = 0) +     scale_color_manual(values = col_phenotype)  grid.arrange(pca_raw, pca_adj, ncol = 1)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"intensity-evaluation-1","dir":"Articles","previous_headings":"Data normalization","what":"Intensity evaluation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"compare RLA plots -sample normalization evaluate impact data.  Figure 33. RLA plot normalization. normalization process effectively centered data around median medians samples now closer zero.","code":"par(mfrow = c(2, 1), mar = c(3.5, 4.5, 2.5, 1))  boxplot(rowRla(assay(res, \"raw_filled\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), cex.main = 1, outline = FALSE,         xaxt = \"n\", main = \"Raw data\", boxwex = 1) grid(nx = NA, ny = NULL) legend(\"topright\", inset = c(0, -0.2), col = col_phenotype,        legend = names(col_phenotype), lty=1, lwd = 2, xpd = TRUE,        ncol = 3, cex = 0.7, bty = \"n\") abline(h = 0, lty=3, lwd = 1, col = \"black\")  boxplot(rowRla(assay(res, \"norm\"), group = res$phenotype),         cex = 0.5, pch = 16, ylab = \"RLA\", border = col_sample,         col = paste0(col_sample, 80), boxwex = 1, outline = FALSE,         xaxt = \"n\", main = \"Normallized data\", cex.main = 1) axis(side = 1, at = seq_len(ncol(res)), labels = res$sample_name) grid(nx = NA, ny = NULL) abline(h = 0, lty = 3, lwd = 1, col = \"black\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"coefficient-of-variation","dir":"Articles","previous_headings":"Data normalization","what":"Coefficient of variation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"next evaluate coefficient variation (CV, also referred relative standard deviation RSD) features across samples either QC study samples. QC samples, CV represent technical noise, study samples include also expected biological differences. Thus, normalization reduce CV QC samples, slightly reducing CV study samples. CV calculated using rowRsd() function MetaboCoreUtils package. setting mad = TRUE use robust calculation using median absolute deviation instead standard deviation. Table 6. Distribution CV values across samples raw normalized data. table shows distribution CV raw normalized data. first column highlights % data given CV value, e.g. 25% data CV equal lower 0.04557 QC_raw data. anticipated, CV values QCs, reflect technical variance, lower compared study samples, include technical biological variance. Overall, minimal disparity exists raw normalized data, positive indication normalization process introduced bias dataset, also reflects little differences average abundances sample raw data.","code":"#' Calculate the CV values index_study <- res$phenotype %in% c(\"CTR\", \"CVD\") index_QC <- res$phenotype == \"QC\"  sample_res <- cbind(     QC_raw = rowRsd(assay(res, \"raw_filled\")[, index_QC],                     na.rm = TRUE, mad = TRUE),     QC_norm = rowRsd(assay(res, \"norm\")[, index_QC],                      na.rm = TRUE, mad = TRUE),     Study_raw = rowRsd(assay(res, \"raw_filled\")[, index_study],                        na.rm = TRUE, mad = TRUE),     Study_norm = rowRsd(assay(res, \"norm\")[, index_study],                         na.rm = TRUE, mad = TRUE) )  #' Summarize the values across features res_df <- data.frame(     QC_raw = quantile(sample_res[, \"QC_raw\"], na.rm = TRUE),     QC_norm = quantile(sample_res[, \"QC_norm\"], na.rm = TRUE),     Study_raw = quantile(sample_res[, \"Study_raw\"], na.rm = TRUE),     Study_norm = quantile(sample_res[, \"Study_norm\"], na.rm = TRUE) )  kable(res_df, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"conclusion-on-normalization","dir":"Articles","previous_headings":"Data normalization","what":"Conclusion on normalization","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"overall conclusion normalization process little variance present beginning, normalization however able center data around median (shown RLA plot). Given simplicity limited size example dataset, conclude normalization process stage. intricate datasets diverse biases, tailored approach devised. include also approaches adjust signal drifts batch effects. One possible option use linear-model based approach can example applied adjust_lm() function MetaboCoreUtils package.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"quality-control-feature-prefiltering","dir":"Articles","previous_headings":"","what":"Quality control: Feature prefiltering","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"normalizing data can now pre-filter clean data performing statistical analysis. general, pre-filtering samples features performed remove outliers. copy original result object also keep unfiltered data later comparisons. eliminate features exhibit high variability dataset. Repeatedly measured QC samples typically serve robust basis cleansing datasets allowing identify features excessively high noise. data set external QC samples used, .e. pooled samples different collection using slightly different sample matrix, utility filtering somewhat limited. comprehensive description guidelines data filtering untargeted metabolomic studies, please refer (Broadhurst et al. 2018). Following guidelines stated decided still use QC samples pre-filtering, basis represent similar bio-fluids study samples, thus, anticipate observing relatively similar metabolites affected similar measurement biases. therefore evaluate dispersion ratio (Dratio) (Broadhurst et al. 2018) features data set. accomplish task using function time DratioFilter parameter. filters exist function invite user explore decide best dataset. Dratio filter powerful tool identify features exhibit high variability data, relating variance observed QC samples study samples. setting threshold 0.4, remove features high degree variability QC study samples. example, feature deviation QC higher 40% (threshold = 0.4)deviation study samples removed. filtering step ensures features retained considerably lower technical biological variance. Note rowDratio() rowRsd() functions MetaboCoreUtils package used calculate actual numeric values estimates used filtering, e.g. evaluate distribution whole data set identify data set-dependent threshold values. Finally, evaluate number features left filtering steps calculate percentage features removed. dataset reduced 8724 4519 features. remove considerable amount features expected want focus reliable features analysis. rest analysis need separate QC samples study samples. store QC samples separate object later use. addition calculate CV QC samples add additional column rowData() result object. used later prioritize identified significant features e.g. low technical noise. Now data set preprocessed, normalized filtered, can start evaluate distribution data estimate variation due biology.","code":"#' Number of features before filtering nrow(res) [1] 8724 #' keep unfiltered object res_unfilt <- res #' Compute and filter based on the Dratio filter_dratio <- DratioFilter(threshold = 0.4,                               qcIndex = res$phenotype == \"QC\",                               studyIndex = res$phenotype != \"QC\",                               mad = TRUE) res <- filterFeatures(res, filter = filter_dratio, assay = \"norm_imputed\") 4205 features were removed #' Number of features after analysis nrow(res) [1] 4519 #' Percentage left: end/beginning nrow(res)/nrow(res_unfilt) * 100 [1] 51.79963 res_qc <- res[, res$phenotype == \"QC\"] res <- res[, res$phenotype != \"QC\"] #' Calculate the QC's CV and add as feature variable to the data set rowData(res)$qc_cv <- assay(res_qc, \"norm\") |>                rowRsd()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"overlapping-features","dir":"Articles","previous_headings":"","what":"Overlapping features","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"possible evalaution features overlapping different sample groups. Fisrt can plot overlapping CVD CTR group. performing complex satistical analysis can evaluate data case large number groups venn diagram can replace upset plot can handle larger number groups, still readable. compare overlap samples.","code":"res_df <- cbind(CVD = rowSums(is.na(assay(res, \"raw_filled\"))) < 10,                  CTR = rowSums(is.na(assay(res, \"raw_filled\"))) < 10)  mat <- data.frame() #make upsetplot"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"differential-abundance-analysis","dir":"Articles","previous_headings":"","what":"Differential abundance analysis","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"normalization quality control, next step identify features differentially abundant study groups. crucial step allows us identify potential biomarkers metabolites associated study groups. various approaches methods available identification features interest. workflow use multiple linear regression analysis identify features significantly difference abundances CVD CTR study group. performing tests evaluate similarities study samples using PCA (excluding QC samples avoid influencing results).  Figure 34. PCA data normalization quality control. samples clearly separate study group PCA indicating differences metabolite profiles two groups. However, drives separation PC1 clear. evaluate whether explained available variable study, .e., age:  Figure 35. PCA colored age data normalization quality control. According PCA , PC1 seem related age. Even variance data set can’t explain stage, proceed (supervised) statistical tests identify features interest. compute linear models metabolite explaining observed feature abundance available study variables. also use base R function lm(), utilize R Biocpkg(\"limma\") package conduct differential abundance analysis: moderated test statistics (Smyth 2004) provided package specifically well suited experiments limited number replicates. tests use linear model ~ phenotype + age, hence explaining abundances one metabolite accounting study group assignment age individual. analysis might benefit inclusion study covariate associated PC2 explaining variance seen principal component, present analysis participant’s age disease association provided. define design study model.matrix() function fit feature-wise linear models log2-transformed abundances using lmFit() function. P-values significance association calculated using eBayes() function, also performs empirical Bayes-based robust estimation standard errors. See also excellent vignette/user guide limma package examples details linear model procedure. linear models fitted, can now proceed extract results. create data frame containing coefficients, raw adjusted p-values (applying Benjamini-Hochberg correction, .e., method = \"BH\" improved control false discovery rate), average intensity signals CVD CTR samples, indication whether feature deemed significant . consider metabolites adjusted p-value smaller 0.05 significant, also include (absolute) difference abundances cut-criteria. last, add differential abundance results result object’s rowData(). can now proceed visualize distribution raw adjusted p-values.  Figure 36. Distribution raw (left) adjusted p-values (right). histograms show distribution raw adjusted p-values. Except enrichment small p-values, raw p-values (less) uniformly distributed, indicates absence strong systematic biases data. adjusted p-values conservative account multiple testing; important fit linear model feature therefore perform large number tests leads high chance false positive findings. see features low p-values, indicating likely significantly different two study groups. plot adjusted p-values log2 fold change (average) abundances. volcano plot allow us visualize features significantly different two study groups. highlighted blue color plot .  Figure 37. Volcano plot showing analysis results. interesting features top corners volcano plot (.e., features large difference abundance groups small p-value). significant features negative coefficient (log2 fold change value) indicating abundance lower CVD samples compared CTR samples. features listed, along average difference (log2) abundance compared groups, adjusted p-values, average (log2) abundance sample group RSD (CV) QC samples table . Table 7. Features significant differences abundances. visualize EICs significant features evaluate (raw) signal. restrict MS data set study samples. Parameters keepFeatures = TRUE: ensures identified features retained subset object. peakBg: defines (background) color individual chromatographic peak EIC object.  Figure 38. Extracted ion chromatograms significant features. EICs significant features show clear single peak. intensities (already observed ) much larger CTR CVD samples. exception second feature (second EIC top row), intensities significant features however generally low. might make challenging identify using LC-MS/MS setup.","code":"#' Define the colors for the plot col_sample <- col_phenotype[res$phenotype]  #' Log transform and scale the data for PCA analysis vals <- assay(res, \"norm_imputed\") |>     t() |>     log2() |>     scale(center = TRUE, scale = TRUE) pca_res <- prcomp(vals, scale = FALSE, center = FALSE)  vals_st <- cbind(vals, phenotype = res$phenotype) autoplot(pca_res, data = vals_st , colour = 'phenotype', scale = 0) +     scale_color_manual(values = col_phenotype) #' Add age to the PCA plot vals_st <- cbind(vals, age = res$age) autoplot(pca_res, data = vals_st , colour = 'age', scale = 0) #' Define the linear model to be applied to the data p.cut <- 0.05     # cut-off for significance. m.cut <- 0.5      # cut-off for log2 fold change  age <- res$age phenotype <- factor(res$phenotype) design <- model.matrix(~ phenotype + age)  #' Fit the linear model to the data, explaining metabolite #' concentrations by phenotype and age. fit <- lmFit(log2(assay(res, \"norm_imputed\")), design = design) fit <- eBayes(fit) #' Compile a result data frame tmp <- data.frame(     coef.CVD = fit$coefficients[, \"phenotypeCVD\"],     pvalue.CVD = fit$p.value[, \"phenotypeCVD\"],     adjp.CVD = p.adjust(fit$p.value[, \"phenotypeCVD\"], method = \"BH\"),     avg.CVD = rowMeans(         log2(assay(res, \"norm_imputed\")[, res$phenotype == \"CVD\"])),     avg.CTR = rowMeans(         log2(assay(res, \"norm_imputed\")[, res$phenotype == \"CTR\"])) ) tmp$significant.CVD <- tmp$adjp.CVD < 0.05 #' Add the results to the object's rowData rowData(res) <- cbind(rowData(res), tmp) #' Plot the distribution of p-values par(mfrow = c(1, 2)) hist(rowData(res)$pvalue.CVD, breaks = 64, xlab = \"p value\",      main = \"Distribution of raw p-values\",      cex.main = 1, cex.lab = 1, cex.axis = 1) hist(rowData(res)$adjp.CVD, breaks = 64, xlab = expression(p[BH]~value),      main = \"Distribution of adjusted p-values\",      cex.main = 1, cex.lab = 1, cex.axis = 1) #' Plot volcano plot of the statistical results par(mfrow = c(1, 1), mar = c(5, 5, 5, 1)) plot(rowData(res)$coef.CVD, -log10(rowData(res)$adjp.CVD),      xlab = expression(log[2]~difference),      ylab = expression(-log[10]~p[BH]), pch = 16, col = \"#00000060\",      cex.main = 1.5, cex.lab = 1.5, cex.axis = 1.3) grid() abline(h = -log10(0.05), col = \"#0000ffcc\") if (any(rowData(res)$significant.CVD)) {     points(rowData(res)$coef.CVD[rowData(res)$significant.CVD],            -log10(rowData(res)$adjp.CVD[rowData(res)$significant.CVD]),            col = \"#0000ffcc\") } # Table of significant features tab <- rowData(res)[rowData(res)$significant.CVD,                     c(\"mzmed\", \"rtmed\", \"coef.CVD\", \"adjp.CVD\",                       \"avg.CTR\", \"avg.CVD\", \"qc_cv\")] |>     as.data.frame() tab <- tab[order(abs(tab$coef.CVD), decreasing = TRUE), ] kable(tab, format = \"pipe\") #' Restrict the raw data to study samples. lcms1_study <- lcms1[sampleData(lcms1)$phenotype != \"QC\", keepFeatures = TRUE] #' Extract EICs for the significant features eic_sign <- featureChromatograms(     lcms1_study, features = rownames(tab), expandRt = 5, filled = TRUE)  #' Plot the EICs. plot(eic_sign, col = col_sample,      peakBg = paste0(col_sample[chromPeaks(eic_sign)[, \"sample\"]], 40)) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"annotation","dir":"Articles","previous_headings":"","what":"Annotation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"now identified features significant differences abundances two study groups. provide information metabolic pathways differentiate affected healthy individuals might hence also serve biomarkers. However, stage analysis know compounds/metabolites actually represent. thus need now annotate signals. Annotation can performed different level confidence Schymanski et al. (2014). lowest level annotation, highest rate false positive hits, bases features m/z ratios. Higher levels annotations employ fragment spectra (MS2 spectra) ions interest requiring however acquisition additional data. section, demonstrate multiple ways annotate significant features using functionality provided Bioconductor packages. Alternative approaches external software tools, may better suited, also discussed later section. data set acquired using LC-MS setup features thus characterized m/z retention times. retention time LC-setup-specific , without prior data/knowledge provide little information features’ identity. Modern MS instruments high accuracy m/z values therefore reliable estimates compound ion’s mass--charge ratio. first approach, use features’ m/z values match reference values, .e., exact masses chemical compounds provided reference database, case MassBank database. full MassBank data re-distributed Bioconductor’s AnnotationHub resource, simplifies integration reproducible R-based analysis workflows. load resource, list available MassBank data sets/releases load one . MassBank data provided self-contained SQLite database data can queried accessed CompoundDb Bioconductor package. use compounds() function extract small compound annotations database. MassBank (small compound annotation databases) provides (exact) molecular mass compound. Since almost small compounds neutral natural state, need first converted m/z values allow matching feature’s m/z. calculate m/z neutral mass, need assume ion (adduct) might generated measured metabolites employed electro-spray ionization. positive polarity, human serum samples, common ions protonated ([M+H]+), bear addition sodium ([M+Na]+) ammonium ([M+H-NH3]+) ions. match observed m/z values reference values potential ions use matchValues() function Mass2MzParam approach, allows specify types expected ions adducts parameter maximal allowed difference compared values using tolerance ppm parameters. first prepare data.frame significant features, set parameters matching perform comparison query features reference database. resulting Matched object shows 4 6 significant features matched ions compounds MassBank database. extract full result Matched object. Thus, total 43 ions compounds MassBank matched significant features based specified tolerance settings. Many compounds, different structure thus function/chemical property, identical chemical formula thus mass. Matching exclusively m/z features hence result many potentially false positive hits thus considered provide low confidence annotation. additional complication annotation resources, like MassBank, community maintained, contain large amount redundant information. reduce redundancy result table iterate hits feature keep matches unique compounds (identified INCHIKEY). INCHI INCHIKEY combine information compound’s chemical formula structure, different compounds can share chemical formula, different structure thus INCHI. Table 9. MS1 annotation results. table shows results MS1-based annotation process. can see four significant features matched. matches seem pretty accurate low ppm errors. deduplication performed considerably reduced number hits feature, first still matches ions large number compounds (chemical formula). Considering features’ m/z retention times MS1-based annotation increase annotation confidence, requires additional data, recording retention time thepure standard compound LC setup. alternative approach might provide better inside annotations help choose different annotations feature evaluate certain chemical properties possible matches. instance, LogP value, available several databases HMDB, provides insight given compound’s polarity. property highly affects interaction analyte column, usually also directly affects separation. Therefore, comparison analyte’s retention time polarity can help rule possible misidentifications. low confidence, MS1-based annotation can provide first candidate annotations confirmed rejected additional analyses. MS1 annotation fast efficient method annotate features therefore give first insight compounds significantly different two study groups. However, always accurate. MS2 data can provide higher level confidence annotation process provides, observed fragmentation pattern, information structure compound. MS2 data can generated LC-MS/MS measurement MS2 spectra recorded ions either data dependent acquisition (DDA) data independent acquisition (DIA) mode. Generally, advised include LC-MS/MS runs QC samples randomly selected study samples already acquisition MS1 data used quantification signals. alternative, addition, post-hoc LC-MS/MS acquisition can performed generate MS2 data needed annotation. present experiment, separate LC-MS/MS measurement conducted QC samples selected study samples generate data using inclusion list pre-selected ions. represent features found significantly different CVD CTR samples initial analysis full experiment. use subset second LC-MS/MS data set show data can used MS2-based annotation. differential abundance analysis found features significantly higher abundances CTR samples. Consequently, utilize MS2 data obtained CTR samples annotate significant features. load LC-MS/MS data experiment restrict data acquired CTR sample. Table 10. Samples LC-MS/MS data set. total 3 LC-MS/MS data files control samples, different collision energy fragment ions. show number MS1 MS2 spectra files. Compared number MS2 spectra, far less MS1 spectra acquired. configuration MS instrument set ensure ions specified inclusion list selected fragmentation, even intensity might low. setting, however, recorded MS2 spectra represent noise. plot shows location precursor ions m/z - retention time plane three files.  can see MS2 spectra recorded m/z interest along full retention time range, even actual ions eluting within certain retention time windows. next extract Spectra object MS data data object assign new spectra variable employed collision energy, extract data object sampleData. next filter MS data first restricting MS2 spectra removing mass peaks spectrum intensity lower 5% highest intensity spectrum, assuming low intensity peaks represent background signal. next remove also mass peaks m/z value greater equal precursor m/z ion. puts, later matching reference spectra, weight fragmentation pattern ions avoids hits based precursor m/z peak (hence similar mass compared compounds). last, restrict data spectra least two fragment peaks scale intensities sum 1 spectrum. similarity calculations affected scaling, makes visual comparison fragment spectra easier read. Finally, also speed later comparison spectra reference database, load full MS data memory (changing backend MsBackendMemory) apply processing steps performed data far. Keeping MS data memory performance benefits, generally suggested large data sets. evaluate impact present data set print addition size data object changing backend. thus moderate increase memory demand loading MS data memory (also filtered cleaned MS2 data). proceed match experimental MS2 spectra reference fragment spectra, workflow aim annotate features found significant differential abundance analysis. goal thus identify MS2 spectra second (LC-MS/MS) run represent fragments ions features data first (LC-MS) run. approach match MS2 spectra significant features determined earlier based precursor m/z retention time (given acceptable tolerance) feature’s m/z retention time. can easily done using featureArea() function effectively considers actual m/z retention time ranges features’ chromatographic peaks therefore increase chance finding correct match. however also assumes retention times first second run don’t differ much. Alternatively, need align retention times second LC-MS/MS data set first. first extract feature area, .e., m/z retention time ranges, significant features. next identify fragment spectra precursor m/z retention times within ranges. use filterRanges() function allows filter Spectra object using multiple ranges simultaneously. apply function separately feature (row matrix) extract MS2 spectra representing fragmentation information presumed feature’s ions. result apply() call list Spectra, element representing result one feature. exception last feature, multiple MS2 spectra identified. next combine list Spectra single Spectra object using concatenateSpectra() function add additional spectra variable containing respective feature identifier. now Spectra object fragment spectra significant features differential expression analysis. next build reference data need process way query spectra. extract fragment spectra MassBank database, restrict positive polarity data (since experiment acquired positive polarity) perform processing fragment spectra MassBank database. Note switch MsBackendMemory backend hence loading full data reference database memory. positive impact performance subsequent spectra matching, however also increase memory demand present analysis. Now Spectra object second run database spectra prepared, can proceed matching process. use matchSpectra() function MetaboAnnotation package CompareSpectraParam define settings matching. following parameters: requirePrecursor = TRUE: Limits spectra similarity calculations fragment spectra similar precursor m/z. tolerance ppm: Defines acceptable difference compared m/z values. relaxed tolerance settings ensure find matches even reference spectra acquired instruments lower accuracy. THRESHFUN: Defines matches report. , keep matches resulting spectra similarity score (calculated normalized dot product (Stein Scott 1994), default similarity function) larger 0.6. Thus, total 315 query MS2 spectra, 16 matched (least) one reference fragment spectrum. restrict results matching spectra extract metadata query target spectra well similarity score (complete list available metadata information can listed colnames() function). Now, query-target pairs spectra similarity higher 0.6. Similar MS1-based annotation also result table contains redundant information: multiple fragment spectra per feature also MassBank contains several fragment spectra compound, measured using differing collision energies MS instruments, different laboratories. thus iterate feature-compound pairs select one highest score. identifier compound, use fragment spectra’s INCHI-key, since compound names MassBank accepted consensus/controlled vocabularies. Table 9.MS2 annotation results. Thus, 5 significant features, one annotated compound based MS2-based approach. many reasons failure find matches features. Although MS2 spectra selected feature, appear represent noise, features, LC-MS/MS run, low MS1 signal recorded, indicating selected sample original compound might (longer) present. Also, reference databases contain predominantly fragment spectra protonated ([M+H]+) ions compounds, features might represent signal types ions result different fragmentation pattern. Finally, fragment spectra compounds interest might also simply present used reference database. Thus, combining information MS1- MS2 based annotation can annotate one feature considerable confidence. feature m/z 195.0879 retention time 32 seconds seems ion caffeine. result somewhat disappointing also clearly shows importance proper experimental planning need control potential confounding factors. present experiment, disease-specific biomarker identified, life-style property individuals suffering disease: coffee consumption probably contraindicated patients CVD group reduce risk heart arrhythmia. plot EIC feature highlighting retention time highest scoring MS2 spectra recorded create mirror plot comparing MS2 spectra reference fragment spectra caffeine.  Figure 39. Extracted ion chromatograms MS2 spectra annotated feature.  Figure 40. MS2 spectra annotated feature. plot clearly shows higher signal feature CTR compared CVD samples. QC samples exhibit lower highly consistent signal, suggesting absence strong technical noise biases raw data experiment. vertical line indicates retention time fragment spectrum best match reference spectrum. noted , since fragment spectra measured separate LC-MS/MS experiment, considered indication approximate retention time ions fragmented second experiment. fragment spectrum feature, shown upper panel right plot highly similar reference spectrum caffeine MassBank (shown lower panel). addition matching precursor m/z, two fragments (m/z intensity) present spectra. can also extract additional metadata matching reference spectrum, used collision energy, fragmentation mode, instrument type, instrument well ion (adduct) fragmented. present workflow highlights annotation performed within R using packages Bioconductor project, also excellent external softwares used alternative, SIRIUS (Dührkop et al. 2019), mummichog (Li et al. 2013) GNPS (Nothias et al. 2020) among others. use , data need exported format supported . MS2 spectra, data easily exported required MGF file format using MsBackendMgf Bioconductor package. Integration xcms feature-based molecular networking GNPS described GNPS documentation. alternative, addition, evidence potential matching chemical formula feature derived evaluating isotope pattern full MS1 scan. provide information isotope composition. Also , various functions isotopologues() MetaboCoreUtils package functionality envipat R package (Loos et al. 2015) used.","code":"#' load reference data ah <- AnnotationHub() #' List available MassBank data sets query(ah, \"MassBank\") AnnotationHub with 6 records # snapshotDate(): 2024-10-28 # $dataprovider: MassBank # $species: NA # $rdataclass: CompDb # additional mcols(): taxonomyid, genome, description, #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, #   rdatapath, sourceurl, sourcetype # retrieve records with, e.g., 'object[[\"AH107048\"]]'               title   AH107048 | MassBank CompDb for release 2021.03   AH107049 | MassBank CompDb for release 2022.06   AH111334 | MassBank CompDb for release 2022.12.1   AH116164 | MassBank CompDb for release 2023.06   AH116165 | MassBank CompDb for release 2023.09   AH116166 | MassBank CompDb for release 2023.11 #' Load one MAssBank release mb <- ah[[\"AH116166\"]] #' Extract compound annotations cmps <- compounds(mb, columns = c(\"name\", \"formula\",                                   \"exactmass\", \"inchikey\")) head(cmps) formula exactmass                    inchikey                 name 1    C27H29NO11  543.1741 AOJJSUZBOXZQNB-UHFFFAOYSA-N           Epirubicin 2      C40H54O4  598.4022 KFNGKYUGHHQDEE-AXWOCEAUSA-N Crassostreaxanthin A 3    C10H24N2O2  204.1838 AEUTYOVWOVBAKS-UWVGGRQHSA-N           Ethambutol 4     C16H27NO5  313.1889 LMFKRLGHEKVMNT-UJDVCPFMSA-N           Heliotrine 5 C20H15Cl3N2OS  435.9971 JLGKQTAYUIMGRK-UHFFFAOYSA-N        Sertaconazole 6      C15H14O5  274.0841 BWNCKEBBYADFPQ-UHFFFAOYSA-N    (R)Semivioxanthin #' Prepare query data frame rowData(res)$feature_id <- rownames(rowData(res)) res_sig <- res[rowData(res)$significant.CVD, ]  #' Setup parameters for the matching param <- Mass2MzParam(adducts = c(\"[M+H]+\", \"[M+Na]+\", \"[M+H-NH3]+\"),                       tolerance = 0, ppm = 5)  #' Perform the matching. mtch <- matchValues(res_sig, cmps, param = param, mzColname = \"mzmed\") mtch Object of class Matched Total number of matches: 43 Number of query objects: 5 (4 matched) Number of target objects: 25685 (43 matched) #' Extracting the results mtch_res <- matchedData(mtch, c(\"feature_id\", \"mzmed\", \"rtmed\",                                 \"adduct\", \"ppm_error\",                                 \"target_formula\", \"target_name\",                                 \"target_inchikey\")) mtch_res DataFrame with 44 rows and 8 columns         feature_id     mzmed     rtmed      adduct ppm_error target_formula        <character> <numeric> <numeric> <character> <numeric>    <character> FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 ...            ...       ...       ...         ...       ...            ... FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1867474      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.0614704      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2 FT1171      FT1171   229.130  181.0883     [M+Na]+ 3.0770838      C12H18N2O          target_name target_inchikey          <character>     <character> FT0371 Benzohydro...   VDEUYMSGMP... FT0371 Trigonelli...   WWNNZCOKKK... FT0371 Salicylami...   SKZKKFZAGN... FT0371 4-Aminoben...   ALYNCZNDIQ... FT0371 Anthranili...   RWZYAGGXGH... ...              ...             ... FT0845      CAFFEINE   RYYVLZVUVI... FT0845      Caffeine   RYYVLZVUVI... FT0845      caffeine   RYYVLZVUVI... FT0845 1,3,7-TRIM...   RYYVLZVUVI... FT1171 Isoproturo...   PUIYMUZLKQ... rownames(mtch_res) <- NULL  #' Keep only info on features that machted - create a utility function for that mtch_res <- split(mtch_res, mtch_res$feature_id) |>     lapply(function(x) {         lapply(split(x, x$target_inchikey), function(z) {             z[which.min(z$ppm_error), ]         })     }) |>     unlist(recursive = FALSE) |>     do.call(what = rbind)  #' Display the results kable(mtch_res, format = \"pipe\") #' Load form the MetaboLights Database param <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                            assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                            \"hilic_metabolite_profiling.txt\"),                            filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                      param,                      keepOntology = FALSE,                      keepProtocol = FALSE,                      simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  # filter samples to keep MSMS data from CTR samples: sampleData(lcms2) <- sampleData(lcms2)[sampleData(lcms2)$phenotype == \"CTR\", ]  sampleData(lcms2) <- sampleData(lcms2)[grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ]  # Add fragmentation data information (from filenames) sampleData(lcms2)$fragmentation_mode <- c(\"CE20\", \"CE30\", \"CES\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, c(10, 240)) #' check the number of spectra per ms level spectra(lcms2) |>     msLevel() |>     split(spectraSampleIndex(lcms2)) |>     lapply(table) |>     do.call(what = cbind) 1    2    3    4   5    6    7    8   9   10   11   12 1 825  186  186  186 825  186  186  186 825  185  186  185 2 825 3121 3118 3124 825 3123 3118 3120 825 3117 3117 3116 plotPrecursorIons(lcms2) ms2_ctr <- spectra(lcms2) ms2_ctr$collision_energy <-     sampleData(lcms2)$fragmentation_mode[spectraSampleIndex(lcms2)] #' Remove low intensity peaks low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 }  ms2_ctr <- filterMsLevel(ms2_ctr, 2L) |>     filterIntensity(intensity = low_int) #' Remove precursor peaks and restrict to spectra with a minimum #' number of peaks ms2_ctr <- filterPrecursorPeaks(ms2_ctr, ppm = 50, mz = \">=\") ms2_ctr <- ms2_ctr[lengths(ms2_ctr) > 1] |>     scalePeaks() #' Size of the object before loading into memory print(object.size(ms2_ctr), units = \"MB\") 5.1 Mb #' Load the MS data subset into memory ms2_ctr <- setBackend(ms2_ctr, MsBackendMemory()) ms2_ctr <- applyProcessing(ms2_ctr)  #' Size of the object after loading into memory print(object.size(ms2_ctr), units = \"MB\") 18.2 Mb #' Define the m/z and retention time ranges for the significant features target <- featureArea(lcms1)[rownames(res_sig), ] target mzmin    mzmax     rtmin     rtmax FT0371 138.0544 138.0552 146.32270 152.86115 FT0565 161.0391 161.0407 159.00234 164.30799 FT0732 182.0726 182.0756  32.71242  42.28755 FT0845 195.0799 195.0887  30.73235  35.67337 FT1171 229.1282 229.1335 178.01450 183.35303 #' Identify for each feature MS2 spectra with their precursor m/z and #' retention time within the feature's m/z and retention time range ms2_ctr_fts <- apply(target[, c(\"rtmin\", \"rtmax\", \"mzmin\", \"mzmax\")],                      MARGIN = 1, FUN = filterRanges, object = ms2_ctr,                      spectraVariables = c(\"rtime\", \"precursorMz\")) lengths(ms2_ctr_fts) FT0371 FT0565 FT0732 FT0845 FT1171     38     36    135     68     38 l <- lengths(ms2_ctr_fts) #' Combine the individual Spectra objects ms2_ctr_fts <- concatenateSpectra(ms2_ctr_fts) #' Assign the feature identifier to each MS2 spectrum ms2_ctr_fts$feature_id <- rep(rownames(res_sig), l) ms2_ref <- Spectra(mb) |>     filterPolarity(1L) |>     filterIntensity(intensity = low_int) |>     filterPrecursorPeaks(ppm = 50, mz = \">=\") ms2_ref <- ms2_ref[lengths(ms2_ref) > 1] |>     scalePeaks() register(SerialParam()) #' Define the settings for the spectra matching. prm <- CompareSpectraParam(ppm = 40, tolerance = 0.05,                            requirePrecursor = TRUE,                            THRESHFUN = function(x) which(x >= 0.6))  ms2_mtch <- matchSpectra(ms2_ctr_fts, ms2_ref, param = prm) ms2_mtch Object of class MatchedSpectra Total number of matches: 214 Number of query objects: 315 (16 matched) Number of target objects: 69561 (21 matched) #' Keep only query spectra with matching reference spectra ms2_mtch <- ms2_mtch[whichQuery(ms2_mtch)]  #' Extract the results ms2_mtch_res <- matchedData(ms2_mtch) nrow(ms2_mtch_res) [1] 214 #' - split the result per feature #' - select for each feature the best matching result for each compound #' - combine the result again into a data frame ms2_mtch_res <-     ms2_mtch_res |>     split(f = paste(ms2_mtch_res$feature_id, ms2_mtch_res$target_inchikey)) |>     lapply(function(z) {         z[which.max(z$score), ]     }) |>     do.call(what = rbind) |>     as.data.frame()  #' List the best matching feature-compound pair pandoc.table(ms2_mtch_res[, c(\"feature_id\", \"target_name\", \"score\",                               \"target_inchikey\")],              style = \"rmarkdown\",              caption = \"Table 9.MS2 annotation results.\",              split.table = Inf) col_sample <- col_phenotype[sampleData(lcms1)$phenotype] #' Extract and plot EIC for the annotated feature eic <- featureChromatograms(lcms1, features = ms2_mtch_res$feature_id[1]) plot(eic, col = col_sample, peakCol = col_sample[chromPeaks(eic)[, \"sample\"]],      peakBg = paste0(col_sample[chromPeaks(eic)[, \"sample\"]], 20)) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1)  #' Identify the best matching query-target spectra pair idx <- which.max(ms2_mtch_res$score)  #' Indicate the retention time of the MS2 spectrum in the EIC plot abline(v = ms2_mtch_res$rtime[idx]) #' Get the index of the MS2 spectrum in the query object query_idx <- which(query(ms2_mtch)$.original_query_index ==                                   ms2_mtch_res$.original_query_index[idx]) query_ms2 <- query(ms2_mtch)[query_idx] #' Get the index of the MS2 spectrum in the target object target_idx <- which(target(ms2_mtch)$spectrum_id ==                                     ms2_mtch_res$target_spectrum_id[idx]) target_ms2 <- target(ms2_mtch)[target_idx]  #' Create a mirror plot comparing the two best matching spectra plotSpectraMirror(query_ms2, target_ms2) legend(\"topleft\",        legend = paste0(\"precursor m/z: \", format(precursorMz(query_ms2), 3))) spectraData(target_ms2, c(\"collisionEnergy_text\", \"fragmentation_mode\",                           \"instrument_type\", \"instrument\", \"adduct\")) |>     as.data.frame() collisionEnergy_text fragmentation_mode instrument_type 1         55 (nominal)                HCD     LC-ESI-ITFT                          instrument adduct 1 LTQ Orbitrap XL Thermo Scientific [M+H]+"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"ms1-based-annotation","dir":"Articles","previous_headings":"","what":"MS1-based annotation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"data set acquired using LC-MS setup features thus characterized m/z retention times. retention time LC-setup-specific , without prior data/knowledge provide little information features’ identity. Modern MS instruments high accuracy m/z values therefore reliable estimates compound ion’s mass--charge ratio. first approach, use features’ m/z values match reference values, .e., exact masses chemical compounds provided reference database, case MassBank database. full MassBank data re-distributed Bioconductor’s AnnotationHub resource, simplifies integration reproducible R-based analysis workflows. load resource, list available MassBank data sets/releases load one . MassBank data provided self-contained SQLite database data can queried accessed CompoundDb Bioconductor package. use compounds() function extract small compound annotations database. MassBank (small compound annotation databases) provides (exact) molecular mass compound. Since almost small compounds neutral natural state, need first converted m/z values allow matching feature’s m/z. calculate m/z neutral mass, need assume ion (adduct) might generated measured metabolites employed electro-spray ionization. positive polarity, human serum samples, common ions protonated ([M+H]+), bear addition sodium ([M+Na]+) ammonium ([M+H-NH3]+) ions. match observed m/z values reference values potential ions use matchValues() function Mass2MzParam approach, allows specify types expected ions adducts parameter maximal allowed difference compared values using tolerance ppm parameters. first prepare data.frame significant features, set parameters matching perform comparison query features reference database. resulting Matched object shows 4 6 significant features matched ions compounds MassBank database. extract full result Matched object. Thus, total 43 ions compounds MassBank matched significant features based specified tolerance settings. Many compounds, different structure thus function/chemical property, identical chemical formula thus mass. Matching exclusively m/z features hence result many potentially false positive hits thus considered provide low confidence annotation. additional complication annotation resources, like MassBank, community maintained, contain large amount redundant information. reduce redundancy result table iterate hits feature keep matches unique compounds (identified INCHIKEY). INCHI INCHIKEY combine information compound’s chemical formula structure, different compounds can share chemical formula, different structure thus INCHI. Table 9. MS1 annotation results. table shows results MS1-based annotation process. can see four significant features matched. matches seem pretty accurate low ppm errors. deduplication performed considerably reduced number hits feature, first still matches ions large number compounds (chemical formula). Considering features’ m/z retention times MS1-based annotation increase annotation confidence, requires additional data, recording retention time thepure standard compound LC setup. alternative approach might provide better inside annotations help choose different annotations feature evaluate certain chemical properties possible matches. instance, LogP value, available several databases HMDB, provides insight given compound’s polarity. property highly affects interaction analyte column, usually also directly affects separation. Therefore, comparison analyte’s retention time polarity can help rule possible misidentifications. low confidence, MS1-based annotation can provide first candidate annotations confirmed rejected additional analyses.","code":"#' load reference data ah <- AnnotationHub() #' List available MassBank data sets query(ah, \"MassBank\") AnnotationHub with 6 records # snapshotDate(): 2024-10-28 # $dataprovider: MassBank # $species: NA # $rdataclass: CompDb # additional mcols(): taxonomyid, genome, description, #   coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags, #   rdatapath, sourceurl, sourcetype # retrieve records with, e.g., 'object[[\"AH107048\"]]'               title   AH107048 | MassBank CompDb for release 2021.03   AH107049 | MassBank CompDb for release 2022.06   AH111334 | MassBank CompDb for release 2022.12.1   AH116164 | MassBank CompDb for release 2023.06   AH116165 | MassBank CompDb for release 2023.09   AH116166 | MassBank CompDb for release 2023.11 #' Load one MAssBank release mb <- ah[[\"AH116166\"]] #' Extract compound annotations cmps <- compounds(mb, columns = c(\"name\", \"formula\",                                   \"exactmass\", \"inchikey\")) head(cmps) formula exactmass                    inchikey                 name 1    C27H29NO11  543.1741 AOJJSUZBOXZQNB-UHFFFAOYSA-N           Epirubicin 2      C40H54O4  598.4022 KFNGKYUGHHQDEE-AXWOCEAUSA-N Crassostreaxanthin A 3    C10H24N2O2  204.1838 AEUTYOVWOVBAKS-UWVGGRQHSA-N           Ethambutol 4     C16H27NO5  313.1889 LMFKRLGHEKVMNT-UJDVCPFMSA-N           Heliotrine 5 C20H15Cl3N2OS  435.9971 JLGKQTAYUIMGRK-UHFFFAOYSA-N        Sertaconazole 6      C15H14O5  274.0841 BWNCKEBBYADFPQ-UHFFFAOYSA-N    (R)Semivioxanthin #' Prepare query data frame rowData(res)$feature_id <- rownames(rowData(res)) res_sig <- res[rowData(res)$significant.CVD, ]  #' Setup parameters for the matching param <- Mass2MzParam(adducts = c(\"[M+H]+\", \"[M+Na]+\", \"[M+H-NH3]+\"),                       tolerance = 0, ppm = 5)  #' Perform the matching. mtch <- matchValues(res_sig, cmps, param = param, mzColname = \"mzmed\") mtch Object of class Matched Total number of matches: 43 Number of query objects: 5 (4 matched) Number of target objects: 25685 (43 matched) #' Extracting the results mtch_res <- matchedData(mtch, c(\"feature_id\", \"mzmed\", \"rtmed\",                                 \"adduct\", \"ppm_error\",                                 \"target_formula\", \"target_name\",                                 \"target_inchikey\")) mtch_res DataFrame with 44 rows and 8 columns         feature_id     mzmed     rtmed      adduct ppm_error target_formula        <character> <numeric> <numeric> <character> <numeric>    <character> FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   2.08055        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 FT0371      FT0371   138.055   148.396      [M+H]+   1.93568        C7H7NO2 ...            ...       ...       ...         ...       ...            ... FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1867474      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.0614704      C8H10N4O2 FT0845      FT0845   195.088   32.6567      [M+H]+ 0.1639884      C8H10N4O2 FT1171      FT1171   229.130  181.0883     [M+Na]+ 3.0770838      C12H18N2O          target_name target_inchikey          <character>     <character> FT0371 Benzohydro...   VDEUYMSGMP... FT0371 Trigonelli...   WWNNZCOKKK... FT0371 Salicylami...   SKZKKFZAGN... FT0371 4-Aminoben...   ALYNCZNDIQ... FT0371 Anthranili...   RWZYAGGXGH... ...              ...             ... FT0845      CAFFEINE   RYYVLZVUVI... FT0845      Caffeine   RYYVLZVUVI... FT0845      caffeine   RYYVLZVUVI... FT0845 1,3,7-TRIM...   RYYVLZVUVI... FT1171 Isoproturo...   PUIYMUZLKQ... rownames(mtch_res) <- NULL  #' Keep only info on features that machted - create a utility function for that mtch_res <- split(mtch_res, mtch_res$feature_id) |>     lapply(function(x) {         lapply(split(x, x$target_inchikey), function(z) {             z[which.min(z$ppm_error), ]         })     }) |>     unlist(recursive = FALSE) |>     do.call(what = rbind)  #' Display the results kable(mtch_res, format = \"pipe\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"ms2-based-annotation","dir":"Articles","previous_headings":"","what":"MS2-based annotation","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"MS1 annotation fast efficient method annotate features therefore give first insight compounds significantly different two study groups. However, always accurate. MS2 data can provide higher level confidence annotation process provides, observed fragmentation pattern, information structure compound. MS2 data can generated LC-MS/MS measurement MS2 spectra recorded ions either data dependent acquisition (DDA) data independent acquisition (DIA) mode. Generally, advised include LC-MS/MS runs QC samples randomly selected study samples already acquisition MS1 data used quantification signals. alternative, addition, post-hoc LC-MS/MS acquisition can performed generate MS2 data needed annotation. present experiment, separate LC-MS/MS measurement conducted QC samples selected study samples generate data using inclusion list pre-selected ions. represent features found significantly different CVD CTR samples initial analysis full experiment. use subset second LC-MS/MS data set show data can used MS2-based annotation. differential abundance analysis found features significantly higher abundances CTR samples. Consequently, utilize MS2 data obtained CTR samples annotate significant features. load LC-MS/MS data experiment restrict data acquired CTR sample. Table 10. Samples LC-MS/MS data set. total 3 LC-MS/MS data files control samples, different collision energy fragment ions. show number MS1 MS2 spectra files. Compared number MS2 spectra, far less MS1 spectra acquired. configuration MS instrument set ensure ions specified inclusion list selected fragmentation, even intensity might low. setting, however, recorded MS2 spectra represent noise. plot shows location precursor ions m/z - retention time plane three files.  can see MS2 spectra recorded m/z interest along full retention time range, even actual ions eluting within certain retention time windows. next extract Spectra object MS data data object assign new spectra variable employed collision energy, extract data object sampleData. next filter MS data first restricting MS2 spectra removing mass peaks spectrum intensity lower 5% highest intensity spectrum, assuming low intensity peaks represent background signal. next remove also mass peaks m/z value greater equal precursor m/z ion. puts, later matching reference spectra, weight fragmentation pattern ions avoids hits based precursor m/z peak (hence similar mass compared compounds). last, restrict data spectra least two fragment peaks scale intensities sum 1 spectrum. similarity calculations affected scaling, makes visual comparison fragment spectra easier read. Finally, also speed later comparison spectra reference database, load full MS data memory (changing backend MsBackendMemory) apply processing steps performed data far. Keeping MS data memory performance benefits, generally suggested large data sets. evaluate impact present data set print addition size data object changing backend. thus moderate increase memory demand loading MS data memory (also filtered cleaned MS2 data). proceed match experimental MS2 spectra reference fragment spectra, workflow aim annotate features found significant differential abundance analysis. goal thus identify MS2 spectra second (LC-MS/MS) run represent fragments ions features data first (LC-MS) run. approach match MS2 spectra significant features determined earlier based precursor m/z retention time (given acceptable tolerance) feature’s m/z retention time. can easily done using featureArea() function effectively considers actual m/z retention time ranges features’ chromatographic peaks therefore increase chance finding correct match. however also assumes retention times first second run don’t differ much. Alternatively, need align retention times second LC-MS/MS data set first. first extract feature area, .e., m/z retention time ranges, significant features. next identify fragment spectra precursor m/z retention times within ranges. use filterRanges() function allows filter Spectra object using multiple ranges simultaneously. apply function separately feature (row matrix) extract MS2 spectra representing fragmentation information presumed feature’s ions. result apply() call list Spectra, element representing result one feature. exception last feature, multiple MS2 spectra identified. next combine list Spectra single Spectra object using concatenateSpectra() function add additional spectra variable containing respective feature identifier. now Spectra object fragment spectra significant features differential expression analysis. next build reference data need process way query spectra. extract fragment spectra MassBank database, restrict positive polarity data (since experiment acquired positive polarity) perform processing fragment spectra MassBank database. Note switch MsBackendMemory backend hence loading full data reference database memory. positive impact performance subsequent spectra matching, however also increase memory demand present analysis. Now Spectra object second run database spectra prepared, can proceed matching process. use matchSpectra() function MetaboAnnotation package CompareSpectraParam define settings matching. following parameters: requirePrecursor = TRUE: Limits spectra similarity calculations fragment spectra similar precursor m/z. tolerance ppm: Defines acceptable difference compared m/z values. relaxed tolerance settings ensure find matches even reference spectra acquired instruments lower accuracy. THRESHFUN: Defines matches report. , keep matches resulting spectra similarity score (calculated normalized dot product (Stein Scott 1994), default similarity function) larger 0.6. Thus, total 315 query MS2 spectra, 16 matched (least) one reference fragment spectrum. restrict results matching spectra extract metadata query target spectra well similarity score (complete list available metadata information can listed colnames() function). Now, query-target pairs spectra similarity higher 0.6. Similar MS1-based annotation also result table contains redundant information: multiple fragment spectra per feature also MassBank contains several fragment spectra compound, measured using differing collision energies MS instruments, different laboratories. thus iterate feature-compound pairs select one highest score. identifier compound, use fragment spectra’s INCHI-key, since compound names MassBank accepted consensus/controlled vocabularies. Table 9.MS2 annotation results. Thus, 5 significant features, one annotated compound based MS2-based approach. many reasons failure find matches features. Although MS2 spectra selected feature, appear represent noise, features, LC-MS/MS run, low MS1 signal recorded, indicating selected sample original compound might (longer) present. Also, reference databases contain predominantly fragment spectra protonated ([M+H]+) ions compounds, features might represent signal types ions result different fragmentation pattern. Finally, fragment spectra compounds interest might also simply present used reference database. Thus, combining information MS1- MS2 based annotation can annotate one feature considerable confidence. feature m/z 195.0879 retention time 32 seconds seems ion caffeine. result somewhat disappointing also clearly shows importance proper experimental planning need control potential confounding factors. present experiment, disease-specific biomarker identified, life-style property individuals suffering disease: coffee consumption probably contraindicated patients CVD group reduce risk heart arrhythmia. plot EIC feature highlighting retention time highest scoring MS2 spectra recorded create mirror plot comparing MS2 spectra reference fragment spectra caffeine.  Figure 39. Extracted ion chromatograms MS2 spectra annotated feature.  Figure 40. MS2 spectra annotated feature. plot clearly shows higher signal feature CTR compared CVD samples. QC samples exhibit lower highly consistent signal, suggesting absence strong technical noise biases raw data experiment. vertical line indicates retention time fragment spectrum best match reference spectrum. noted , since fragment spectra measured separate LC-MS/MS experiment, considered indication approximate retention time ions fragmented second experiment. fragment spectrum feature, shown upper panel right plot highly similar reference spectrum caffeine MassBank (shown lower panel). addition matching precursor m/z, two fragments (m/z intensity) present spectra. can also extract additional metadata matching reference spectrum, used collision energy, fragmentation mode, instrument type, instrument well ion (adduct) fragmented.","code":"#' Load form the MetaboLights Database param <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                            assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                            \"hilic_metabolite_profiling.txt\"),                            filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                      param,                      keepOntology = FALSE,                      keepProtocol = FALSE,                      simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  # filter samples to keep MSMS data from CTR samples: sampleData(lcms2) <- sampleData(lcms2)[sampleData(lcms2)$phenotype == \"CTR\", ]  sampleData(lcms2) <- sampleData(lcms2)[grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ]  # Add fragmentation data information (from filenames) sampleData(lcms2)$fragmentation_mode <- c(\"CE20\", \"CE30\", \"CES\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, c(10, 240)) #' check the number of spectra per ms level spectra(lcms2) |>     msLevel() |>     split(spectraSampleIndex(lcms2)) |>     lapply(table) |>     do.call(what = cbind) 1    2    3    4   5    6    7    8   9   10   11   12 1 825  186  186  186 825  186  186  186 825  185  186  185 2 825 3121 3118 3124 825 3123 3118 3120 825 3117 3117 3116 plotPrecursorIons(lcms2) ms2_ctr <- spectra(lcms2) ms2_ctr$collision_energy <-     sampleData(lcms2)$fragmentation_mode[spectraSampleIndex(lcms2)] #' Remove low intensity peaks low_int <- function(x, ...) {     x > max(x, na.rm = TRUE) * 0.05 }  ms2_ctr <- filterMsLevel(ms2_ctr, 2L) |>     filterIntensity(intensity = low_int) #' Remove precursor peaks and restrict to spectra with a minimum #' number of peaks ms2_ctr <- filterPrecursorPeaks(ms2_ctr, ppm = 50, mz = \">=\") ms2_ctr <- ms2_ctr[lengths(ms2_ctr) > 1] |>     scalePeaks() #' Size of the object before loading into memory print(object.size(ms2_ctr), units = \"MB\") 5.1 Mb #' Load the MS data subset into memory ms2_ctr <- setBackend(ms2_ctr, MsBackendMemory()) ms2_ctr <- applyProcessing(ms2_ctr)  #' Size of the object after loading into memory print(object.size(ms2_ctr), units = \"MB\") 18.2 Mb #' Define the m/z and retention time ranges for the significant features target <- featureArea(lcms1)[rownames(res_sig), ] target mzmin    mzmax     rtmin     rtmax FT0371 138.0544 138.0552 146.32270 152.86115 FT0565 161.0391 161.0407 159.00234 164.30799 FT0732 182.0726 182.0756  32.71242  42.28755 FT0845 195.0799 195.0887  30.73235  35.67337 FT1171 229.1282 229.1335 178.01450 183.35303 #' Identify for each feature MS2 spectra with their precursor m/z and #' retention time within the feature's m/z and retention time range ms2_ctr_fts <- apply(target[, c(\"rtmin\", \"rtmax\", \"mzmin\", \"mzmax\")],                      MARGIN = 1, FUN = filterRanges, object = ms2_ctr,                      spectraVariables = c(\"rtime\", \"precursorMz\")) lengths(ms2_ctr_fts) FT0371 FT0565 FT0732 FT0845 FT1171     38     36    135     68     38 l <- lengths(ms2_ctr_fts) #' Combine the individual Spectra objects ms2_ctr_fts <- concatenateSpectra(ms2_ctr_fts) #' Assign the feature identifier to each MS2 spectrum ms2_ctr_fts$feature_id <- rep(rownames(res_sig), l) ms2_ref <- Spectra(mb) |>     filterPolarity(1L) |>     filterIntensity(intensity = low_int) |>     filterPrecursorPeaks(ppm = 50, mz = \">=\") ms2_ref <- ms2_ref[lengths(ms2_ref) > 1] |>     scalePeaks() register(SerialParam()) #' Define the settings for the spectra matching. prm <- CompareSpectraParam(ppm = 40, tolerance = 0.05,                            requirePrecursor = TRUE,                            THRESHFUN = function(x) which(x >= 0.6))  ms2_mtch <- matchSpectra(ms2_ctr_fts, ms2_ref, param = prm) ms2_mtch Object of class MatchedSpectra Total number of matches: 214 Number of query objects: 315 (16 matched) Number of target objects: 69561 (21 matched) #' Keep only query spectra with matching reference spectra ms2_mtch <- ms2_mtch[whichQuery(ms2_mtch)]  #' Extract the results ms2_mtch_res <- matchedData(ms2_mtch) nrow(ms2_mtch_res) [1] 214 #' - split the result per feature #' - select for each feature the best matching result for each compound #' - combine the result again into a data frame ms2_mtch_res <-     ms2_mtch_res |>     split(f = paste(ms2_mtch_res$feature_id, ms2_mtch_res$target_inchikey)) |>     lapply(function(z) {         z[which.max(z$score), ]     }) |>     do.call(what = rbind) |>     as.data.frame()  #' List the best matching feature-compound pair pandoc.table(ms2_mtch_res[, c(\"feature_id\", \"target_name\", \"score\",                               \"target_inchikey\")],              style = \"rmarkdown\",              caption = \"Table 9.MS2 annotation results.\",              split.table = Inf) col_sample <- col_phenotype[sampleData(lcms1)$phenotype] #' Extract and plot EIC for the annotated feature eic <- featureChromatograms(lcms1, features = ms2_mtch_res$feature_id[1]) plot(eic, col = col_sample, peakCol = col_sample[chromPeaks(eic)[, \"sample\"]],      peakBg = paste0(col_sample[chromPeaks(eic)[, \"sample\"]], 20)) legend(\"topright\", col = col_phenotype, legend = names(col_phenotype), lty = 1)  #' Identify the best matching query-target spectra pair idx <- which.max(ms2_mtch_res$score)  #' Indicate the retention time of the MS2 spectrum in the EIC plot abline(v = ms2_mtch_res$rtime[idx]) #' Get the index of the MS2 spectrum in the query object query_idx <- which(query(ms2_mtch)$.original_query_index ==                                   ms2_mtch_res$.original_query_index[idx]) query_ms2 <- query(ms2_mtch)[query_idx] #' Get the index of the MS2 spectrum in the target object target_idx <- which(target(ms2_mtch)$spectrum_id ==                                     ms2_mtch_res$target_spectrum_id[idx]) target_ms2 <- target(ms2_mtch)[target_idx]  #' Create a mirror plot comparing the two best matching spectra plotSpectraMirror(query_ms2, target_ms2) legend(\"topleft\",        legend = paste0(\"precursor m/z: \", format(precursorMz(query_ms2), 3))) spectraData(target_ms2, c(\"collisionEnergy_text\", \"fragmentation_mode\",                           \"instrument_type\", \"instrument\", \"adduct\")) |>     as.data.frame() collisionEnergy_text fragmentation_mode instrument_type 1         55 (nominal)                HCD     LC-ESI-ITFT                          instrument adduct 1 LTQ Orbitrap XL Thermo Scientific [M+H]+"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"external-tools-or-alternative-annotation-approaches","dir":"Articles","previous_headings":"","what":"External tools or alternative annotation approaches","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"present workflow highlights annotation performed within R using packages Bioconductor project, also excellent external softwares used alternative, SIRIUS (Dührkop et al. 2019), mummichog (Li et al. 2013) GNPS (Nothias et al. 2020) among others. use , data need exported format supported . MS2 spectra, data easily exported required MGF file format using MsBackendMgf Bioconductor package. Integration xcms feature-based molecular networking GNPS described GNPS documentation. alternative, addition, evidence potential matching chemical formula feature derived evaluating isotope pattern full MS1 scan. provide information isotope composition. Also , various functions isotopologues() MetaboCoreUtils package functionality envipat R package (Loos et al. 2015) used.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"tutorial, describe end--end workflow LC-MS-based untargeted metabolomics experiments, conducted entirely within R using packages Bioconductor project base R functionality. excellent software exists perform similar analyses, power R-based workflow lies adaptability individual data sets research questions ability build reproducible workflows documentation. Due space restrictions don’t provide comprehensive listing methodologies individual analysis steps. advanced options approaches available, e.g., normalization data, however also heavily dependent size properties analyzed data set, well annotation features. result, found present analysis set features significant abundance differences compared groups. however reliably annotate single feature, related lifestyle individuals rather pathological properties investigated disease. low proportion annotated signals however uncommon untargeted metabolomics experiments reflects need comprehensive reliable reference annotation libraries.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"session-information","dir":"Articles","previous_headings":"","what":"Session information","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"","code":"sessionInfo() R version 4.4.2 (2024-10-31) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 24.04.1 LTS  Matrix products: default BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0  locale:  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C  [9] LC_ADDRESS=C               LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C  time zone: Etc/UTC tzcode source: system (glibc)  attached base packages: [1] stats4    stats     graphics  grDevices utils     datasets  methods [8] base  other attached packages:  [1] MetaboAnnotation_1.10.1     CompoundDb_1.10.0  [3] AnnotationFilter_1.30.0     AnnotationHub_3.14.0  [5] BiocFileCache_2.14.0        dbplyr_2.5.0  [7] gridExtra_2.3               ggfortify_0.4.17  [9] ggplot2_3.5.1               vioplot_0.5.0 [11] zoo_1.8-12                  sm_2.2-6.0 [13] pheatmap_1.0.12             RColorBrewer_1.1-3 [15] pander_0.6.5                limma_3.62.1 [17] MetaboCoreUtils_1.14.0      xcms_4.5.1 [19] MsBackendMetaboLights_1.0.0 Spectra_1.16.0 [21] BiocParallel_1.40.0         alabaster.se_1.6.0 [23] alabaster.base_1.6.1        SummarizedExperiment_1.36.0 [25] Biobase_2.66.0              GenomicRanges_1.58.0 [27] GenomeInfoDb_1.42.1         IRanges_2.40.0 [29] S4Vectors_0.44.0            BiocGenerics_0.52.0 [31] MatrixGenerics_1.18.0       matrixStats_1.4.1 [33] MsIO_0.0.8                  MsExperiment_1.8.0 [35] ProtGenerics_1.38.0         readxl_1.4.3 [37] BiocStyle_2.34.0            quarto_1.4.4 [39] knitr_1.49  loaded via a namespace (and not attached):   [1] later_1.4.1                 bitops_1.0-9   [3] filelock_1.0.3              tibble_3.2.1   [5] cellranger_1.1.0            preprocessCore_1.68.0   [7] XML_3.99-0.17               lifecycle_1.0.4   [9] doParallel_1.0.17           processx_3.8.4  [11] lattice_0.22-6              MASS_7.3-61  [13] MultiAssayExperiment_1.32.0 magrittr_2.0.3  [15] rmarkdown_2.29              yaml_2.3.10  [17] MsCoreUtils_1.18.0          DBI_1.2.3  [19] abind_1.4-8                 zlibbioc_1.52.0  [21] purrr_1.0.2                 RCurl_1.98-1.16  [23] rappdirs_0.3.3              GenomeInfoDbData_1.2.13  [25] MSnbase_2.32.0              ncdf4_1.23  [27] codetools_0.2-20            DelayedArray_0.32.0  [29] xml2_1.3.6                  DT_0.33  [31] tidyselect_1.2.1            farver_2.1.2  [33] UCSC.utils_1.2.0            base64enc_0.1-3  [35] jsonlite_1.8.9              iterators_1.0.14  [37] foreach_1.5.2               tools_4.4.2  [39] progress_1.2.3              Rcpp_1.0.13-1  [41] glue_1.8.0                  SparseArray_1.6.0  [43] xfun_0.49                   dplyr_1.1.4  [45] HDF5Array_1.34.0            withr_3.0.2  [47] BiocManager_1.30.25         fastmap_1.2.0  [49] rhdf5filters_1.18.0         fansi_1.0.6  [51] digest_0.6.37               mime_0.12  [53] R6_2.5.1                    colorspace_2.1-1  [55] rsvg_2.6.1                  RSQLite_2.3.8  [57] utf8_1.2.4                  tidyr_1.3.1  [59] generics_0.1.3              prettyunits_1.2.0  [61] PSMatch_1.10.0              httr_1.4.7  [63] htmlwidgets_1.6.4           S4Arrays_1.6.0  [65] pkgconfig_2.0.3             gtable_0.3.6  [67] blob_1.2.4                  impute_1.80.0  [69] MassSpecWavelet_1.72.0      XVector_0.46.0  [71] htmltools_0.5.8.1           MALDIquant_1.22.3  [73] clue_0.3-66                 scales_1.3.0  [75] alabaster.matrix_1.6.1      png_0.1-8  [77] rstudioapi_0.17.1           reshape2_1.4.4  [79] rjson_0.2.23                curl_6.0.1  [81] cachem_1.1.0                rhdf5_2.50.0  [83] stringr_1.5.1               BiocVersion_3.20.0  [85] parallel_4.4.2              AnnotationDbi_1.68.0  [87] mzID_1.44.0                 vsn_3.74.0  [89] pillar_1.9.0                grid_4.4.2  [91] alabaster.schemas_1.6.0     vctrs_0.6.5  [93] MsFeatures_1.14.0           pcaMethods_1.98.0  [95] cluster_2.1.6               evaluate_1.0.1  [97] cli_3.6.3                   compiler_4.4.2  [99] rlang_1.1.4                 crayon_1.5.3 [101] labeling_0.4.3              QFeatures_1.16.0 [103] ChemmineR_3.58.0            ps_1.8.1 [105] affy_1.84.0                 plyr_1.8.9 [107] fs_1.6.5                    stringi_1.8.4 [109] munsell_0.5.1               Biostrings_2.74.0 [111] lazyeval_0.2.2              Matrix_1.7-1 [113] hms_1.1.3                   bit64_4.5.2 [115] Rhdf5lib_1.28.0             KEGGREST_1.46.0 [117] statmod_1.5.0               alabaster.ranges_1.6.0 [119] mzR_2.40.0                  igraph_2.1.1 [121] memoise_2.0.1               affyio_1.76.0 [123] bit_4.5.0"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/a-end-to-end-untargeted-metabolomics.html","id":"aknowledgment","dir":"Articles","previous_headings":"","what":"Aknowledgment","title":"Complete end-to-end LC-MS/MS Metabolomic Data analysis","text":"Thanks Steffen Neumann continuous work develop maintain xcms software…","code":""},{"path":[]},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"certain experiments, aligning datasets recorded different times necessary. can involve comparing runs samples different laboratories matching MS2 data initial MS1 run. Variation retention time across laboratories LC systems often requires alignment step using adjustRtime() LamaParama parameter. described data description vignette, samples run twice: LC-MS mode LC-MS/MS mode. tutorial show align LC-MS/MS run preprocessed LC-MS dataset. following packages needed: Setting parallel processing improve efficiency process: First, let’s load pre-processed LC-MS object. xcms result object created End--end worflow vignette also available metabonaut R package. result object (XcmsExperiment object) stored using Bioconductor’s alabaster framework load object using readMsObject() function providing path stored data. import function also takes care eventually retrieving missing MS data files MetaboLights repository. Next, load unprocessed LC-MS/MS data MetaboLights database: adjust sampleData() LC-MS/MS object make easier access: Table 1. Samples LC-MS/MS data set. keep MS runs (MS/MS) remove pooled samples, focusing samples E common runs. alignment, ensure retention time (RT) ranges match datasets: need adjust RT range LC-MS/MS object match LC-MS data: evaluate retention time shifts, ’ll plot base peak chromatogram (BPC): Compare run1 sample run2 sample  Similarly, compare BPC sample E:  Perform peak detection refining alignment, detailed end--end vignette. setting applied. Now, attempt align two samples previous dataset. first step extract landmark features (referred lamas). achieve , identify features present every phenotype group lcms1 dataset. , categorize (using factor()) data phenotype retain QC samples. variable utilized filter features using PercentMissingFilter parameter within filterFeatures() function. , setting threshold = 0 select features present QC samples. lamas input look like alignment. terms method works, alignment algorithm matches chromatographic peaks experimental data lamas, fitting model based match adjust retention times minimize differences two datasets. Now can define parameter object LamaParama prepare alignment. Parameters tolerance, toleranceRt, ppm relate matching chromatographic peaks lamas. parameters related type fitting generated data points. details parameter overall method can found searching ?adjustRtime. example using default parameters. matchLamaChromPeaks() function facilitates assessment well lamas correspond chromatographic peaks file. extract matched results using matchedRtimes() function. used later evaluate alignment. Now can adjust retention time LC-MS/MS dataset using adjustRtime() function. extract base peak chromatogram (BPC) aligned object: evaluate performance alignment process, generate plots comparing alignment reference dataset (black) LC-MS data (red) (blue) adjustment.   Although overall matching imperfect due initial sample issues, certain regions show significant improvement. alignment signal’s start particularly well done. Specifically, regions right 150 seconds show substantial improvement. visualization distribution chromatographic peaks matched anchor peaks (lamas) Sample . red vertical lines represent positions matched peaks.  quantitatively assess quality alignment, compute distance chromatographic peaks LC-MS data anchor peaks (Lamas) alignment.  Furthermore, detailed examination matching model used fitting file possible. Numerical information can obtained using summarizeLamaMatch() function. , percentage chromatographic peaks utilized alignment can computed relative total number peaks file. Additionally, feasible directly plot() param object file interest, showcasing distribution chromatographic peaks along fitted model line.  tutorial demonstrated align LC-MS LC-MS/MS datasets correct retention time shifts, crucial handling data different runs platforms. preprocessed data, detected chromatographic peaks, used landmark features (lamas) QC samples adjust retention times via adjustRtime() function. Visual comparisons base peak chromatograms alignment, along distance calculations, showed clear improvements RT synchronization. Ultimately, aligning chromatographic data ensures subsequent analyses, feature extraction statistical comparisons, based consistent time points, improving data quality reliability. tutorial outlined end--end workflow can adapted various LC-MS-based metabolomics studies, helping researchers manage retention time variation effectively.","code":"library(MsIO) library(alabaster.se) library(MsBackendMetaboLights) library(xcms) library(MsExperiment) library(Spectra) library(vioplot) #' Set up parallel processing using 2 cores if (.Platform$OS.type == \"unix\") {     register(MulticoreParam(2)) } else {     register(SnowParam(2)) } lcms1 <- readMsObject(     XcmsExperiment(),     AlabasterParam(system.file(\"extdata\", \"preprocessed_lcms1\",                                package = \"metabonaut\"))) #' Load form the MetaboLights Database mlp <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                          assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                                             \"hilic_metabolite_profiling.txt\"),                          filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                       mlp,                       keepOntology = FALSE,                       keepProtocol = FALSE,                       simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") # Only keep MS run lcms2 <- lcms2[!grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ] range(rtime(lcms1)) [1]   9.674428 240.115311 range(rtime(lcms2)) [1]   0.275 480.176 #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, range(rtime(lcms1))) idx_A <- which(sampleData(lcms1)$sample_name == \"A\") idx_E <- which(sampleData(lcms1)$sample_name == \"E\") bpc1 <-chromatogram(lcms1[c(idx_A,idx_E)], aggregationFun = \"max\",                     msLevel = 1) bpc2 <- chromatogram(lcms2, aggregationFun = \"max\", msLevel = 1) plot(bpc1[1, 1], col = \"#00000080\",      main = \"BPC sample A LC-MS vs A LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 1]), intensity(bpc2[1, 1]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") plot(bpc1[1, 2], col = \"#00000080\",      main = \"BPC sample E LC-MS vs E LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") cwp <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms2 <- findChromPeaks(lcms2, param = cwp, chunkSize = 2) mnpp <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75) lcms2 <- refineChromPeaks(lcms2, param = mnpp, chunkSize = 2) f <- sampleData(lcms1)$phenotype f[f != \"QC\"] <- NA lcms1 <- filterFeatures(lcms1, PercentMissingFilter(threshold = 0, f = f),                         filled = FALSE) 3694 features were removed lcms1_mz_rt <- featureDefinitions(lcms1)[, c(\"mzmed\",\"rtmed\")] head(lcms1_mz_rt) mzmed    rtmed FT0001 50.98979 203.6001 FT0002 51.05904 191.1675 FT0003 51.98657 203.1467 FT0004 53.02036 203.2343 FT0005 53.52080 203.1936 FT0007 54.01010 235.9032 nrow(lcms1_mz_rt) [1] 5374 lp <- LamaParama(lamas = lcms1_mz_rt, method = \"loess\", span = 0.5,                  outlierTolerance = 3, zeroWeight = 10, ppm =20,                  tolerance = 0, toleranceRt = 20, bs = \"tp\") lp <- matchLamasChromPeaks(lcms2, param = lp) ref_vs_obs <- matchedRtimes(lp) #' input into `adjustRtime()` lcms2 <- adjustRtime(lcms2, param = lp) lcms2 <- applyAdjustedRtime(lcms2) bpc2_adj <- chromatogram(lcms2, aggregationFun = \"max\",                          msLevel = 1) #' BPC of sample A par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1,1]), intensity(bpc2[1,1]),        type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 1], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1,1]), intensity(bpc2_adj[1,1]),        type = \"l\",        col = \"#0000ff80\") #' BPC of sample B par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 2], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 2], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1, 2]), intensity(bpc2_adj[1, 2]), type = \"l\",        col = \"#0000ff80\") #' BPC of the first sample with matches to lamas overlay par(mfrow = c(1, 1)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Distribution CP matched to Lamas\",      lwd = 1.5,      peakType = \"none\") points(rtime(bpc2_adj[1, 1]), intensity(bpc2_adj[1, 1]), type = \"l\",        col = \"#0000ff80\") grid() abline(v = ref_vs_obs[[1]]$obs, col = \"#c4114510\") # Extract data for sample 3 directly ref_obs_sample_1 <- ref_vs_obs[[\"1\"]]  # Calculate distances before and after alignment dist_before <- abs(ref_obs_sample_1$obs - ref_obs_sample_1$ref) dist_after <- abs(chromPeaks(lcms2)[ref_obs_sample_1$chromPeaksId,                                              \"rt\"] - ref_obs_sample_1$ref)  # Create a data frame for plotting distances <- data.frame(   Distance = c(dist_before, dist_after),   Alignment = rep(c(\"Before\", \"After\"), each = length(dist_before)) )  # Set factor levels for Alignment to ensure correct order distances$Alignment <- factor(distances$Alignment, levels = c(\"Before\", \"After\"))  # Plot distances between anchor peaks between the two runs before and after alignment. vioplot(Distance ~ Alignment, data = distances, xlab = \"\",         rectCol = \"#c4114580\",         lineCol = \"white\",         col=\"#17138fe8\",         border = \"white\",         ylab = \"Distance (s)\",         main = \"Distance to Anchor Peaks: Before vs. After Alignment\") #' Access summary of matches and model information summary <- summarizeLamaMatch(lp) summary Total_peaks Matched_peaks Total_lamas Model_summary 1        6832          1825        5374  1666, c(.... 2        6860          1785        5374  1617, c(.... 3        7588          2082        5374  1869, c(.... #' Coverage for each file summary$Matched_peaks / summary$Total_peaks * 100 [1] 26.71253 26.02041 27.43806 #' Access the information on the model of for the first file summary$Model_summary[[1]] Call: loess(formula = ref ~ obs, data = rt_map, weights = weights,     span = span)  Number of Observations: 1666 Equivalent Number of Parameters: 7.38 Residual Standard Error: 2.315 Trace of smoother matrix: 8.13  (exact)  Control settings:   span     :  0.5   degree   :  2   family   :  gaussian   surface  :  interpolate     cell = 0.2   normalize:  TRUE  parametric:  FALSE drop.square:  FALSE #' Plot obs vs. lcms1 with fitting line plot(lp, index = 1L, main = \"ChromPeaks versus Lamas for sample A\",      colPoint = \"red\") Warning in rbind(deparse.level, ...): number of columns of result, 3, is not a multiple of vector length 2 of arg 1 abline(0, 1, lty = 3, col = \"grey\") grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"load-preprocessed-lc-ms-object","dir":"Articles","previous_headings":"","what":"Load preprocessed LC-MS object","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"First, let’s load pre-processed LC-MS object. xcms result object created End--end worflow vignette also available metabonaut R package. result object (XcmsExperiment object) stored using Bioconductor’s alabaster framework load object using readMsObject() function providing path stored data. import function also takes care eventually retrieving missing MS data files MetaboLights repository.","code":"lcms1 <- readMsObject(     XcmsExperiment(),     AlabasterParam(system.file(\"extdata\", \"preprocessed_lcms1\",                                package = \"metabonaut\")))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"load-unprocessed-lc-msms-data","dir":"Articles","previous_headings":"","what":"Load unprocessed LC-MS/MS data","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"Next, load unprocessed LC-MS/MS data MetaboLights database: adjust sampleData() LC-MS/MS object make easier access: Table 1. Samples LC-MS/MS data set. keep MS runs (MS/MS) remove pooled samples, focusing samples E common runs. alignment, ensure retention time (RT) ranges match datasets: need adjust RT range LC-MS/MS object match LC-MS data:","code":"#' Load form the MetaboLights Database mlp <- MetaboLightsParam(mtblsId = \"MTBLS8735\",                          assayName = paste0(\"a_MTBLS8735_LC-MSMS_positive_\",                                             \"hilic_metabolite_profiling.txt\"),                          filePattern = \".mzML\")  lcms2 <- readMsObject(MsExperiment(),                       mlp,                       keepOntology = FALSE,                       keepProtocol = FALSE,                       simplify = TRUE) #adjust sampleData colnames(sampleData(lcms2)) <- c(\"sample_name\", \"derived_spectra_data_file\",                                 \"metabolite_asssignment_file\",                                 \"source_name\",                                 \"organism\",                                 \"blood_sample_type\",                                 \"sample_type\", \"age\", \"unit\", \"phenotype\")  #let's look at the updated sample data sampleData(lcms2)[, c(\"derived_spectra_data_file\",                      \"phenotype\", \"sample_name\", \"age\")] |>     kable(format = \"pipe\") # Only keep MS run lcms2 <- lcms2[!grepl(\"MSMS\", sampleData(lcms2)$derived_spectra_data_file), ] range(rtime(lcms1)) [1]   9.674428 240.115311 range(rtime(lcms2)) [1]   0.275 480.176 #' Filter the data to the same RT range as the LC-MS run lcms2 <- filterRt(lcms2, range(rtime(lcms1)))"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"comparing-chromatograms","dir":"Articles","previous_headings":"","what":"Comparing chromatograms","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"evaluate retention time shifts, ’ll plot base peak chromatogram (BPC): Compare run1 sample run2 sample  Similarly, compare BPC sample E:","code":"idx_A <- which(sampleData(lcms1)$sample_name == \"A\") idx_E <- which(sampleData(lcms1)$sample_name == \"E\") bpc1 <-chromatogram(lcms1[c(idx_A,idx_E)], aggregationFun = \"max\",                     msLevel = 1) bpc2 <- chromatogram(lcms2, aggregationFun = \"max\", msLevel = 1) plot(bpc1[1, 1], col = \"#00000080\",      main = \"BPC sample A LC-MS vs A LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 1]), intensity(bpc2[1, 1]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\") plot(bpc1[1, 2], col = \"#00000080\",      main = \"BPC sample E LC-MS vs E LC-MS/MS\", lwd = 1.5, peakType = \"none\") grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), col = \"#0000ff80\", type = \"l\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"peak-detection","dir":"Articles","previous_headings":"","what":"Peak detection","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"Perform peak detection refining alignment, detailed end--end vignette. setting applied.","code":"cwp <- CentWaveParam(peakwidth = c(1, 8), ppm = 15, integrate = 2) lcms2 <- findChromPeaks(lcms2, param = cwp, chunkSize = 2) mnpp <- MergeNeighboringPeaksParam(expandRt = 2.5, expandMz = 0.0015,                                     minProp = 0.75) lcms2 <- refineChromPeaks(lcms2, param = mnpp, chunkSize = 2)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"alignment","dir":"Articles","previous_headings":"","what":"Alignment","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"Now, attempt align two samples previous dataset. first step extract landmark features (referred lamas). achieve , identify features present every phenotype group lcms1 dataset. , categorize (using factor()) data phenotype retain QC samples. variable utilized filter features using PercentMissingFilter parameter within filterFeatures() function. , setting threshold = 0 select features present QC samples. lamas input look like alignment. terms method works, alignment algorithm matches chromatographic peaks experimental data lamas, fitting model based match adjust retention times minimize differences two datasets. Now can define parameter object LamaParama prepare alignment. Parameters tolerance, toleranceRt, ppm relate matching chromatographic peaks lamas. parameters related type fitting generated data points. details parameter overall method can found searching ?adjustRtime. example using default parameters. matchLamaChromPeaks() function facilitates assessment well lamas correspond chromatographic peaks file. extract matched results using matchedRtimes() function. used later evaluate alignment. Now can adjust retention time LC-MS/MS dataset using adjustRtime() function.","code":"f <- sampleData(lcms1)$phenotype f[f != \"QC\"] <- NA lcms1 <- filterFeatures(lcms1, PercentMissingFilter(threshold = 0, f = f),                         filled = FALSE) 3694 features were removed lcms1_mz_rt <- featureDefinitions(lcms1)[, c(\"mzmed\",\"rtmed\")] head(lcms1_mz_rt) mzmed    rtmed FT0001 50.98979 203.6001 FT0002 51.05904 191.1675 FT0003 51.98657 203.1467 FT0004 53.02036 203.2343 FT0005 53.52080 203.1936 FT0007 54.01010 235.9032 nrow(lcms1_mz_rt) [1] 5374 lp <- LamaParama(lamas = lcms1_mz_rt, method = \"loess\", span = 0.5,                  outlierTolerance = 3, zeroWeight = 10, ppm =20,                  tolerance = 0, toleranceRt = 20, bs = \"tp\") lp <- matchLamasChromPeaks(lcms2, param = lp) ref_vs_obs <- matchedRtimes(lp) #' input into `adjustRtime()` lcms2 <- adjustRtime(lcms2, param = lp) lcms2 <- applyAdjustedRtime(lcms2)"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"evaluation","dir":"Articles","previous_headings":"","what":"Evaluation","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"extract base peak chromatogram (BPC) aligned object: evaluate performance alignment process, generate plots comparing alignment reference dataset (black) LC-MS data (red) (blue) adjustment.   Although overall matching imperfect due initial sample issues, certain regions show significant improvement. alignment signal’s start particularly well done. Specifically, regions right 150 seconds show substantial improvement. visualization distribution chromatographic peaks matched anchor peaks (lamas) Sample . red vertical lines represent positions matched peaks.  quantitatively assess quality alignment, compute distance chromatographic peaks LC-MS data anchor peaks (Lamas) alignment.  Furthermore, detailed examination matching model used fitting file possible. Numerical information can obtained using summarizeLamaMatch() function. , percentage chromatographic peaks utilized alignment can computed relative total number peaks file. Additionally, feasible directly plot() param object file interest, showcasing distribution chromatographic peaks along fitted model line.","code":"bpc2_adj <- chromatogram(lcms2, aggregationFun = \"max\",                          msLevel = 1) #' BPC of sample A par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1,1]), intensity(bpc2[1,1]),        type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 1], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1,1]), intensity(bpc2_adj[1,1]),        type = \"l\",        col = \"#0000ff80\") #' BPC of sample B par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 2], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 2], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1, 2]), intensity(bpc2_adj[1, 2]), type = \"l\",        col = \"#0000ff80\") #' BPC of the first sample with matches to lamas overlay par(mfrow = c(1, 1)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Distribution CP matched to Lamas\",      lwd = 1.5,      peakType = \"none\") points(rtime(bpc2_adj[1, 1]), intensity(bpc2_adj[1, 1]), type = \"l\",        col = \"#0000ff80\") grid() abline(v = ref_vs_obs[[1]]$obs, col = \"#c4114510\") # Extract data for sample 3 directly ref_obs_sample_1 <- ref_vs_obs[[\"1\"]]  # Calculate distances before and after alignment dist_before <- abs(ref_obs_sample_1$obs - ref_obs_sample_1$ref) dist_after <- abs(chromPeaks(lcms2)[ref_obs_sample_1$chromPeaksId,                                              \"rt\"] - ref_obs_sample_1$ref)  # Create a data frame for plotting distances <- data.frame(   Distance = c(dist_before, dist_after),   Alignment = rep(c(\"Before\", \"After\"), each = length(dist_before)) )  # Set factor levels for Alignment to ensure correct order distances$Alignment <- factor(distances$Alignment, levels = c(\"Before\", \"After\"))  # Plot distances between anchor peaks between the two runs before and after alignment. vioplot(Distance ~ Alignment, data = distances, xlab = \"\",         rectCol = \"#c4114580\",         lineCol = \"white\",         col=\"#17138fe8\",         border = \"white\",         ylab = \"Distance (s)\",         main = \"Distance to Anchor Peaks: Before vs. After Alignment\") #' Access summary of matches and model information summary <- summarizeLamaMatch(lp) summary Total_peaks Matched_peaks Total_lamas Model_summary 1        6832          1825        5374  1666, c(.... 2        6860          1785        5374  1617, c(.... 3        7588          2082        5374  1869, c(.... #' Coverage for each file summary$Matched_peaks / summary$Total_peaks * 100 [1] 26.71253 26.02041 27.43806 #' Access the information on the model of for the first file summary$Model_summary[[1]] Call: loess(formula = ref ~ obs, data = rt_map, weights = weights,     span = span)  Number of Observations: 1666 Equivalent Number of Parameters: 7.38 Residual Standard Error: 2.315 Trace of smoother matrix: 8.13  (exact)  Control settings:   span     :  0.5   degree   :  2   family   :  gaussian   surface  :  interpolate     cell = 0.2   normalize:  TRUE  parametric:  FALSE drop.square:  FALSE #' Plot obs vs. lcms1 with fitting line plot(lp, index = 1L, main = \"ChromPeaks versus Lamas for sample A\",      colPoint = \"red\") Warning in rbind(deparse.level, ...): number of columns of result, 3, is not a multiple of vector length 2 of arg 1 abline(0, 1, lty = 3, col = \"grey\") grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"visualizing-alignment-quality","dir":"Articles","previous_headings":"Introduction","what":"Visualizing Alignment Quality","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"evaluate performance alignment process, generate plots comparing alignment reference dataset (black) LC-MS data (red) (blue) adjustment.   Although overall matching imperfect due initial sample issues, certain regions show significant improvement. alignment signal’s start particularly well done. Specifically, regions right 150 seconds show substantial improvement. visualization distribution chromatographic peaks matched anchor peaks (lamas) Sample . red vertical lines represent positions matched peaks.","code":"#' BPC of sample A par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1,1]), intensity(bpc2[1,1]),        type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 1], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1,1]), intensity(bpc2_adj[1,1]),        type = \"l\",        col = \"#0000ff80\") #' BPC of sample B par(mfrow = c(2, 1),  mar = c(2.5, 2.5, 2.5, 0.5), mgp = c(1.5, 0.5, 0)) plot(bpc1[1, 2], col = \"#00000080\", main = \"Before Alignment\", lwd = 1.5,      peakType = \"none\", xlab = NA) grid() points(rtime(bpc2[1, 2]), intensity(bpc2[1, 2]), type = \"l\",        col = \"#0000ff80\") legend(\"topleft\", col = c(\"#00000080\", \"#0000ff80\"),        legend = c(\"LC-MS\", \"LC-MS/MS\"), lty = 1, lwd = 2, horiz = TRUE, bty = \"n\")  plot(bpc1[1, 2], col = \"#00000080\", main = \"After Alignment\", lwd = 1.5,      peakType = \"none\", xlab = \"rtime (s)\") grid() points(rtime(bpc2_adj[1, 2]), intensity(bpc2_adj[1, 2]), type = \"l\",        col = \"#0000ff80\") #' BPC of the first sample with matches to lamas overlay par(mfrow = c(1, 1)) plot(bpc1[1, 1], col = \"#00000080\", main = \"Distribution CP matched to Lamas\",      lwd = 1.5,      peakType = \"none\") points(rtime(bpc2_adj[1, 1]), intensity(bpc2_adj[1, 1]), type = \"l\",        col = \"#0000ff80\") grid() abline(v = ref_vs_obs[[1]]$obs, col = \"#c4114510\")"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"quantitative-evaluation-of-alignment","dir":"Articles","previous_headings":"Introduction","what":"Quantitative Evaluation of Alignment","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"quantitatively assess quality alignment, compute distance chromatographic peaks LC-MS data anchor peaks (Lamas) alignment.  Furthermore, detailed examination matching model used fitting file possible. Numerical information can obtained using summarizeLamaMatch() function. , percentage chromatographic peaks utilized alignment can computed relative total number peaks file. Additionally, feasible directly plot() param object file interest, showcasing distribution chromatographic peaks along fitted model line.","code":"# Extract data for sample 3 directly ref_obs_sample_1 <- ref_vs_obs[[\"1\"]]  # Calculate distances before and after alignment dist_before <- abs(ref_obs_sample_1$obs - ref_obs_sample_1$ref) dist_after <- abs(chromPeaks(lcms2)[ref_obs_sample_1$chromPeaksId,                                              \"rt\"] - ref_obs_sample_1$ref)  # Create a data frame for plotting distances <- data.frame(   Distance = c(dist_before, dist_after),   Alignment = rep(c(\"Before\", \"After\"), each = length(dist_before)) )  # Set factor levels for Alignment to ensure correct order distances$Alignment <- factor(distances$Alignment, levels = c(\"Before\", \"After\"))  # Plot distances between anchor peaks between the two runs before and after alignment. vioplot(Distance ~ Alignment, data = distances, xlab = \"\",         rectCol = \"#c4114580\",         lineCol = \"white\",         col=\"#17138fe8\",         border = \"white\",         ylab = \"Distance (s)\",         main = \"Distance to Anchor Peaks: Before vs. After Alignment\") #' Access summary of matches and model information summary <- summarizeLamaMatch(lp) summary Total_peaks Matched_peaks Total_lamas Model_summary 1        6832          1825        5374  1666, c(.... 2        6860          1785        5374  1617, c(.... 3        7588          2082        5374  1869, c(.... #' Coverage for each file summary$Matched_peaks / summary$Total_peaks * 100 [1] 26.71253 26.02041 27.43806 #' Access the information on the model of for the first file summary$Model_summary[[1]] Call: loess(formula = ref ~ obs, data = rt_map, weights = weights,     span = span)  Number of Observations: 1666 Equivalent Number of Parameters: 7.38 Residual Standard Error: 2.315 Trace of smoother matrix: 8.13  (exact)  Control settings:   span     :  0.5   degree   :  2   family   :  gaussian   surface  :  interpolate     cell = 0.2   normalize:  TRUE  parametric:  FALSE drop.square:  FALSE #' Plot obs vs. lcms1 with fitting line plot(lp, index = 1L, main = \"ChromPeaks versus Lamas for sample A\",      colPoint = \"red\") Warning in rbind(deparse.level, ...): number of columns of result, 3, is not a multiple of vector length 2 of arg 1 abline(0, 1, lty = 3, col = \"grey\") grid()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/alignment-to-external-dataset.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"Seamless Alignment: Merging New Data with and Existing Preprocessed Dataset","text":"tutorial demonstrated align LC-MS LC-MS/MS datasets correct retention time shifts, crucial handling data different runs platforms. preprocessed data, detected chromatographic peaks, used landmark features (lamas) QC samples adjust retention times via adjustRtime() function. Visual comparisons base peak chromatograms alignment, along distance calculations, showed clear improvements RT synchronization. Ultimately, aligning chromatographic data ensures subsequent analyses, feature extraction statistical comparisons, based consistent time points, improving data quality reliability. tutorial outlined end--end workflow can adapted various LC-MS-based metabolomics studies, helping researchers manage retention time variation effectively.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/install_v0.html","id":"running-workflows-locally","dir":"Articles","previous_headings":"","what":"Running workflows locally","title":"Install","text":"install computer packages necessary workflows run code follow: get error message downloading form GitHub might due expired token. Remove using code try install :","code":"install.packages(\"BiocManager\") BiocManager::install('RforMassSpectrometry/MsIO', ask = FALSE,                       dependencies = TRUE)  BiocManager::install(\"RforMassSpectrometry/metabonaut\",                      dependencies = TRUE, ask = FALSE, update = TRUE) #See if any token is there: gitcreds::gitcreds_get()  # Delete them: gitcreds::gitcreds_delete()"},{"path":"https://rformassspectrometry.github.io/metabonaut/articles/install_v0.html","id":"docker-image","dir":"Articles","previous_headings":"","what":"Docker image","title":"Install","text":"vignettes files along R runtime environment including required packages RStudio (Posit) editor bundled docker container. installation, docker container can run computer code examples vignettes can evaluated within environment (without need install additional packages files). don’t already , install docker. Find installation information . Get docker image tutorial e.g. command line : Start docker container, either Docker Desktop, command line Enter http://localhost:8787 web browser log username rstudio password bioc. RStudio server version: open Quarto files vignettes folder evaluate R code blocks document.","code":"docker pull rformassspectrometry/metabonaut:latest docker run -e PASSWORD=bioc -p 8787:8787 rformassspectrometry/metabonaut:latest"},{"path":"https://rformassspectrometry.github.io/metabonaut/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Philippine Louail. Author, maintainer.           ORCID: 0009-0007-5429-6846 Anna Tagliaferri. Contributor.           ORCID: 0009-0001-4044-4285 Vinicius Verri Hernandes. Contributor.           ORCID: 0000-0002-3057-6460 Daniel Marques de Sá e Silva. Contributor.           ORCID: 0000-0002-9674-042X Johannes Rainer. Author.           ORCID: 0000-0002-6977-7147","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Philippine Louail, & Johannes Rainer. (2024). Streamlining LC-MS/MS Data Analysis R Open-Source xcms RforMassSpectrometry: End--End Workflow (Version v1).Zenodo. https://doi.org/10.5281/zenodo.11370612","code":"@Manual{,   title = {Streamlining LC-MS/MS Data Analysis in R with Open-Source xcms and RforMassSpectrometry: An End-to-End Workflow},   author = {Philippine Louail and Johannes Rainer},   publisher = {Zenodo},   year = {2024},   month = {may},   version = {v1.1.0},   doi = {10.5281/zenodo.11370612},   url = {https://doi.org/10.5281/zenodo.11370612}, }"},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"lets-explore-and-learn-to-analyze-untargeted-metabolomics-data","dir":"","previous_headings":"","what":"Exploring and Analyzing LC-MS Data","title":"Exploring and Analyzing LC-MS Data","text":"Welcome Metabonaut! 🧑‍🚀 initiative presents series workflows based small LC-MS/MS dataset, utilizing R Bioconductor packages. Throughout workflows, demonstrate adapt various algorithms specific datasets seamlessly integrate R packages ensure efficient, reproducible processing. primary workflow “Complete End--End LC-MS/MS Metabolomic Data Analysis”. full R code examples, along detailed descriptions, available end--end-untargeted-metabolomics.qmd file. file can opened RStudio, allowing execute individual R command. vignettes website interlinked, can find detailed description dataset used throughout . strive reproducibility. workflows designed remain stable time, allowing run vignettes together one comprehensive “super-vignette”. major change document smaller updates check News","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"for-r-beginners","dir":"","previous_headings":"","what":"For R beginners","title":"Exploring and Analyzing LC-MS Data","text":"tutorials provided assume users basic knowledge R RMarkdown. ’re unfamiliar either, recommend completing short tutorial help test code adapt data. vignettes written Quarto format, learn go , farily new format, functionallity shared RMarkdown format, therefore learning can usefull . basic R course documentation, recommand check website try interactive course fun introduction basic R programming. cheatsheet also help.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"known-issues","dir":"","previous_headings":"","what":"Known Issues","title":"Exploring and Analyzing LC-MS Data","text":"just beginning Metabonaut journey, website still refined. ’re actively addressing ongoing issues. ’re aware problem, ’ll list . Currently, known issues code. encounter , please ensure latest versions required packages (detailed ). issue persists, please report reproducible example GitHub . encounter issues, don’t hesitate let us know!","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"contribution","dir":"","previous_headings":"","what":"Contribution","title":"Exploring and Analyzing LC-MS Data","text":"contributions, please see RforMassSpectrometry contributions guideline.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Exploring and Analyzing LC-MS Data","text":"Please review RforMassSpectrometry Code Conduct.","code":""},{"path":[]},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.6","title":"metabonaut 0.0","text":"Moving PercentMissing filtering end pre-processing steps needs done normalization. Addition collapsing code improve readability. Reduction table size","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-1","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.5","title":"metabonaut 0.0","text":"Require MsIO version 0.0.8 allow reading stored MsBackendMetaboLights objects. simplify import previously stored result object avoid using variable name param every parameter object","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-2","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.4","title":"metabonaut 0.0","text":"Required alabaster.se. Removal Spectra data depth visualisation move Data investigation vignette Removal internal standard matching features Normalization part. Save load lcms1 res object end--end workflow used Seamless Alignment vignette. Using MsIO alabaster.se","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-3","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.3","title":"metabonaut 0.0","text":"Require xcms version 4.3.4 install package github.","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-4","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.2","title":"metabonaut 0.0","text":"Switch Quarto instead Rmarkdown Addition Alignment reference dataset vignette Addition Data investigation vignette Addition Install vignette","code":""},{"path":"https://rformassspectrometry.github.io/metabonaut/news/index.html","id":"changes-in-0-0-5","dir":"Changelog","previous_headings":"","what":"Changes in 0.0.1","title":"metabonaut 0.0","text":"Addition basic files workflow package. Addition end--end vignette.","code":""}]
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