GSEnrichAnalyze: Main function of gene set enrichment analysis

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

Description

The main function of gene set enrichment analysis

Usage

GSEnrichAnalyze(gene.set, gene.score, gene.score.perm, weighted.type = 1)

Arguments

gene.set	a SeqGeneSet object.
gene.score	a vector of integrated gene scores in the same order as genes listed in the geneList slot of gene.set.
gene.score.perm	a matrix of integrated gene scores on permutation data sets; row: genes; col: permutation.
weighted.type	weight type for gene scores; default: 1.

Value

A SeqGeneSet object with many slots updated, such as GSEA.ES and GSEA.pval.

Author(s)

Xi Wang, [e-mail]

References

Xi Wang and Murray J. Cairns (2013). Gene Set Enrichment Analysis of RNA-Seq Data: Integrating Differential Expression and Splicing. BMC Bioinformatics, 14(Suppl 5):S16.

See Also

normES, signifES

Examples

data(DEscore, package="SeqGSEA")
data(DSscore, package="SeqGSEA")
gene.score <- geneScore(DEscore, DSscore, method="linear", DEweight = 0.3)
data(DEscore.perm, package="SeqGSEA")
data(DSscore.perm, package="SeqGSEA")
gene.score.perm <- genePermuteScore(DEscore.perm, DSscore.perm, method="linear",  DEweight=0.3)
data(GS_example, package="SeqGSEA") 
GS_example <- GSEnrichAnalyze(GS_example, gene.score, gene.score.perm)
topGeneSets(GS_example, 5)

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