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

[Package systemPipeR version 1.26.3 Index]