run_edgeR: Runs edgeR

Description

Convenience wrapper function to identify differentially expressed genes (DEGs) in batch mode with the edgeR GML method for any number of pairwise sample comparisons specified under the cmp argument. Users are strongly encouraged to consult the edgeR vignette for more detailed information on this topic and how to properly run edgeR on data sets with more complex experimental designs.

Usage

run_edgeR(countDF, targets, cmp, independent = TRUE, paired = NULL, mdsplot = "")

Arguments

countDF

date.frame containing raw read counts

targets

targets data.frame

cmp

character matrix where comparisons are defined in two columns. This matrix should be generated with readComp() from the targets file. Values used for comparisons need to match those in the Factor column of the targets file.

independent

If independent=TRUE then the countDF will be subsetted for each comparison. This behavior can be useful when working with samples from unrelated studies. For samples from the same or comparable studies, the setting independent=FALSE is usually preferred.

paired

Defines pairs (character vector) for paired analysis. Default is unpaired (paired=NULL).

mdsplot

Directory where plotMDS should be written to. Default setting mdsplot="" will omit the plotting step.

Value

data.frame containing edgeR results from all comparisons. Comparison labels are appended to column titles for tracking.

References

Please properly cite the edgeR papers when using this function: http://www.bioconductor.org/packages/devel/bioc/html/edgeR.html

Examples

Run this code

targetspath <- system.file("extdata", "targets.txt", package="systemPipeR")
targets <- read.delim(targetspath, comment="#")
cmp <- readComp(file=targetspath, format="matrix", delim="-")
countfile <- system.file("extdata", "countDFeByg.xls", package="systemPipeR")
countDF <- read.delim(countfile, row.names=1)
edgeDF <- run_edgeR(countDF=countDF, targets=targets, cmp=cmp[[1]], independent=FALSE, mdsplot="")
pval <- edgeDF[, grep("_FDR$", colnames(edgeDF)), drop=FALSE]
fold <- edgeDF[, grep("_logFC$", colnames(edgeDF)), drop=FALSE]
DEG_list <- filterDEGs(degDF=edgeDF, filter=c(Fold=2, FDR=10))
names(DEG_list)
DEG_list$Summary