GOplotTools

Tools to conduct functional enrichment analysis, parse and plot output from gene ontology (GO) analysis (web) tools

User functions

• runGprofiler(): Run GO analysis locally with the g:GOSt function of g:Profiler
• plotGprofilerDots(): Generate lollipop plot of results obtained with runGprofiler() or from web interface. If the latter, manually add column 'log2Enr' with log2 (enrichment).
• plotGprofilerMulti(): Generate heatmap of results obtained with multi-query runGprofiler()
• plotFuncAssDots(): Generate lollipop plot of results obtained with FuncAssociate
• plotFuncAssCategories(): Plot FuncAssociate enrichment scores as bar graph
• plotDAVIDclusters(): Plot DAVID cluster scores as bar graph

Dependencies

• Any version of R
• CRAN packags gprofiler2 (for local g:Profiler analysis), plotrix and optionally parallel
• CRAN package pheatmap for plotGprofilerDots()

Workflow to conduct and plot GO analysis using g:Profiler or FuncAssociate

1. Generate a list of (foreground) genes and a suitable background

A suitable background often is not the whole genome but should represent the set of genes that could be found in the analysis, e.g. because they contain alternative splicing events or are expressed in the control sample. It is better to use a unique gene identifier such as the ENCODE gene ID rather than the gene name. Gene names change.

2a. Upload to g:Profiler, FuncAssociate (or DAVID) and run appropriate analysis

Find g:Profiler, FuncAssociate and DAVID.

If using the web interface of g:Profiler, add a column log2Enr with the log2 enrichment. If using FuncAssociate, save the results table as well as the 'Attribute/Entity List'.
Examples can be found in the input folder.

OR

2b. Run g:Profiler analysis locally

using runGprofiler(), which uses the gProfiler2 package (Kolberg et al., F1000Research 2020). See arguments to change species, data sources etc.

3. Plot results using one of the plotting functions

For examples of 'lollipop' plots based on g:Profiler and FuncAssociate see output folder.

g:Profiler: Huge categories will be removed and only 'highlighted' driver categories will be shown by default. See options. Filtered categories will be plotted such that log2-enrichment is on the x-axis, dot size represents the number of genes from the category that were in the foreground, and color reflects p-value. Sources are indicated by text color.

See arguments to tweak filtering behaviour etc.

source("GOplotTools.R")

foreground <- read.delim("input/Input_genesWithChangingExons.txt", header=FALSE)[,1]
background <- read.delim("input/Input_background.txt", header=FALSE)[,1]

enriched <- runGprofiler(fore = foreground, back = background, species = "mmusculus", outBase = NA)

plotGprofilerDots(over    = enriched$result,
                  main    = "Genes enriched in input over background",
                  outName = "output/gProfiler_plotGprofilerDots.pdf", 
                  wid = 7, hei = 3.5)

FuncAssociate: Huge categories will be removed by default. If categories overlap more than a threshold, only the more significant one will be kept. See options. Remaining categories will be plotted such that the LOD is on the x-axis, dot size represents the number of genes from the category that were in the foreground, and color reflects p-value:

source("GOplotTools.R")

plotFuncAssDots(file        = "input/FuncAssociate_results.tsv",
                outName     = "output/FuncAssociate_plotFuncAssDots.pdf",
                inputGenes  = "input/Input_genesWithChangingExons.txt",
                attrEntList = "input/FuncAssociate_attrEntList.xls",
                main = "Genes enriched in input over background",
                wid = 9, hei = 4.5)

Disclaimer:

Input checking is imperfect. If it doesn't work, check that you are submitting the correct files.