genePermuteScore: Calculate gene scores on permutation data sets

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

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, [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

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)

SeqGSEA documentation built on Nov. 8, 2020, 5:46 p.m.