run_DESeq2: Runs DESeq2
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 DESeq2 for any number of pairwise sample comparisons specified under the cmp argument. Users are strongly encouraged to consult the DESeq2 vignette for more detailed information on this topic and how to properly run DESeq2 on data sets with more complex experimental designs.

1	run_DESeq2(countDF, targets, cmp, independent = FALSE)

`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 the `readComp()` function 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.

data.frame containing DESeq2 results from all comparisons. Comparison labels are appended to column titles for tracking.

Thomas Girke

Please properly cite the DESeq2 papers when using this function: http://www.bioconductor.org/packages/devel/bioc/html/DESeq2.html

run_edgeR, readComp and DESeq2 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)
degseqDF <- run_DESeq2(countDF=countDF, targets=targets, cmp=cmp[[1]], independent=FALSE)
pval <- degseqDF[, grep("_FDR$", colnames(degseqDF)), drop=FALSE]
fold <- degseqDF[, grep("_logFC$", colnames(degseqDF)), drop=FALSE]
DEG_list <- filterDEGs(degDF=degseqDF, filter=c(Fold=2, FDR=10))
names(DEG_list)
DEG_list$Summary