run_edgeR: Runs edgeR
In systemPipeR: systemPipeR: NGS workflow and report generation environment

Description Usage Arguments Value Author(s) References See Also Examples

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.

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

`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.

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

Thomas Girke

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

run_DESeq2, readComp and edgeR vignette

targetspath <- system.file("extdata", "targets.txt", package="systemPipeR")
targets <- read.delim(targetspath, comment.char = "#")
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