run_DESeq2 {systemPipeR}

R Documentation

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, lfcShrink=FALSE, type="normal")

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.
lfcShrink	logiacal. If TRUE adds shrunken log2 fold changes (LFC) to the object.
type	please check DESeq2::lfcShrink() documentation. Available character alternatives: "apeglm"; "ashr"; "normal".

Value

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

Author(s)

Thomas Girke

References

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

See Also

run_edgeR, readComp and DESeq2 vignette

Examples

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

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