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

Description Usage Arguments Details Value Author(s) References See Also Examples

Integration of differential expression and differential splice scores with a rank-based strategy, which simultaneously integrates observed scores and permutation scores using the same ranks.

1	rankCombine(DEscore, DSscore, DEscoreMat, DSscoreMat, DEweight = 0.5)

`DEscore`	differential expression scores, normalized.
`DSscore`	differential splice scores, normalized.
`DEscoreMat`	differential expression scores in permuted data sets, normalized.
`DSscoreMat`	differential splice scores in permuted data sets, normalized.
`DEweight`	any number between 0 and 1 (included), the weight of differential expression scores (so the weight for differential splice is (1-DEweight)).

This integration method is also known as integration with global ranks. See Wang and Cairns (2013) for details.

A list with two elements geneScore and genePermuteScore.

Xi Wang, xi.wang@newcastle.edu.au

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.

geneScore, genePermuteScore

data(DEscore, package="SeqGSEA")
data(DSscore, package="SeqGSEA")
data(DEscore.perm, package="SeqGSEA")
data(DSscore.perm, package="SeqGSEA")
combine <- rankCombine(DEscore, DSscore, DEscore.perm, DSscore.perm, DEweight=0.3) 
gene.score <- combine$geneScore
gene.score.perm <- combine$genePermuteScore