run_DESeq2: Runs DESeq2

Description

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.

Usage

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

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 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.

Value

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

References

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