README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# qPCRstat

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The goal of `qPCRstat` is to provide an easier way to process qPCR data for graphical output. Meanwhile, some functions included is generic and good for other use.

This package includes functions to calculate the fold change using either ddCt method or Paffl method, and function to calculate SEM and check outliers in a given dataset.

## Installation

Check [qPCRstat](https://dev.yiximeta.com/resources/code/Rpackage/qPCRstat/) for latest version.

To install the current version, follow the instruction here:

1.  Download the binary package from [here](https://dev.yiximeta.com/resources/code/Rpackage/qPCRstat/latest/);

2.  Then in a R session, type the following code (remember to replace the path to the actual file path on your computer):

``` r
PATH <- "/path/to/qPCRstat_version.tar.gz"
install.packages(PATH, repos = NULL, type ="source")
```

Alternatively, install via github directly in R console:

    # install.packages("remotes")
    remotes::install_github("YixiBio/qPCRstat")

Installation via CRAN is in consideration.

## Example

```{r example}
library(qPCRstat)
## sample data
set.seed(1)
Ct1 <- sample(c(35:12), 10, replace = TRUE)
set.seed(1)
Ct2 <- sample(c(12:35), 10, replace = TRUE)
dat <- data.frame(Sample = c(sample("sample", 9, replace = TRUE), "control"), Ct1 = Ct1, Ct2 = Ct2)

## calculate the FC
dat <- cbind(dat[dat$Sample == "sample",], 
             FC = ddCt.raw(dat[dat$Sample == "sample", "Ct1"], 
                           dat[dat$Sample == "sample", "Ct2"], 
                           dat[dat$Sample == "control", "Ct1"],
                           dat[dat$Sample == "control", "Ct2"]))

## check outliers
dat <- cbind(dat, Outliers = is.outlier(dat$FC))

## [Optional] Remove outliers, optionally save outliers in another data frame. 
dat.outlier <- dat[dat$Outliers == TRUE,]
dat.pure <- dat[dat$Outliers == FALSE,]

## Calculate the mean and mean±SEM
mean(dat[dat$Outliers == FALSE, "FC"])
error.bar(dat[dat$Outliers == FALSE, "FC"])

## graphical output using plot() or ggplot2 package
```

## Limitations

-   To process the data, other package (e.g. `dplyr`) is needed.
-   Data might need to be structured with Excel or equivalent.

## To-Do

-   Add function to automatic digest a structured dataset and provide ready-to-use data frames for graphical output.