runDESeq: Run DESeq for differential expression analysis

In sunlightwang/SeqGSEA: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing

Description

This function provides a wrapper to run DESeq for differential expression analysis. It includes two steps, DESeq::estimateSizeFactors and DESeq::estimateDispersions.

Usage

runDESeq(geneCounts, label)

Arguments

geneCounts	a matrix containing read counts for each gene, can be the output of getGeneCount.
label	the sample classification labels.

Value

A DESeqDataSet object with size factors and dispersion parameters been estimated.

Author(s)

Xi Wang, [e-mail]

References

Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data, Genome Biol, 11, R106.

See Also

getGeneCount, DENBTest, DENBStat4GSEA

Examples

data(RCS_example, package="SeqGSEA")
geneCounts <- getGeneCount(RCS_example)
label <- label(RCS_example)
dds <- runDESeq(geneCounts, label)

sunlightwang/SeqGSEA documentation built on Dec. 5, 2020, 5:52 p.m.