genePermuteScore {SeqGSEA} | R Documentation

## Calculate gene scores on permutation data sets

### Description

Calculate gene scores on permutation data sets

### Usage

    genePermuteScore(DEscoreMat, DSscoreMat = NULL, method = c("linear", "quadratic", "rank"),
                     DEweight = 0.5)

### Arguments

`DEscoreMat` | normalized DE scores on permutation data sets.
`DSscoreMat` | normalized DS scores on permutation data sets.
`method` | one of the integration methods: linear, quadratic, or rank; default: linear.
`DEweight` | any number between 0 and 1 (included), the weight of differential expression scores (the weight for differential splice is (1-DEweight)).

### Details

The integration methods including "linear", "quadratic", and "rank" are detailed in Wang and Cairns (2013). Here the rank method refers only to the method using data-set-specific ranks.

For DE-only analysis, just specify DEweight to be 1, and the DSscoreMat value can be NULL.

### Value

A gene score matrix.

### 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

`geneScore`

### Examples

    data(DEscore.perm, package="SeqGSEA")
    data(DSscore.perm, package="SeqGSEA")
    # linear combination with weight for DE 0.3
    gene.score.perm <- genePermuteScore(DEscore.perm, DSscore.perm, method="linear",  DEweight=0.3)
    # DE only analysis
    gene.score.perm <- genePermuteScore(DEscore.perm, DEweight=1)

[Package SeqGSEA version 1.26.0 Index]