GSEnrichAnalyze {SeqGSEA} | R Documentation

## Main function of gene set enrichment analysis

### 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, xi.wang@newcastle.edu.au

### 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)

[Package SeqGSEA version 1.20.0 Index]