pairwiseGsea: GSEA on a pair of phenotypes
In HTSanalyzeR: Gene set over-representation, enrichment and network analyses for high-throughput screens

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

This function performs pairwise GSEA: it looks for gene sets that are specifically over-represented towards the two different ends of two ranked phenotype vectors, in a gene set collection.

1 2	pairwiseGsea(gl1, gl2, gsc, exponent=1, nPermutations=1000, minGeneSetSize=15, pAdjustMethod="BH")

`gl1`	a named numeric or integer vector where names are gene identifiers of the same type as the ones in the gene set collection, and values are the measurements on phenotype one corresponding to those genes. This vector MUST be ordered (decreasing or increasing)
`gl2`	a named numeric or integer vector where names are gene identifiers of the same type as the ones in the gene set collection, and values are the measurements on phenotype two corresponding to those genes. This vector MUST be ordered
`gsc`	a list of gene sets, each of which in the list is a character vector of gene identifiers.
`exponent`	a single numeric or integer value (set as 1 by default) specifying the exponent of the GSEA method.
`nPermutations`	a single numeric or integer value specifying the number of permutation tests for each gene set
`minGeneSetSize`	a single numeric or integer value specifying the minimum size required for a gene set to be considered.
`pAdjustMethod`	a single character value specifying the p-value adjustment method to be used (see 'p.adjust' for details)

phenotypes one and two must be measured on the same genes, i.e. the two vectors gl1 and gl2 must have the same length and their names must match, but the two vectors must be ordered separately, i.e.one phenotype vector is ordered based on the values of that phenotype only

a table with a row for each gene set, containing the p-values for the GSEA, and the observed scores for each of the phenotypes independently. The table is ordered by the p-value column.

Camille Terfve, Xin Wang

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S. & Mesirov, J. P. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550.

pairwiseGseaPlot, pairwisePhenoMannWhit

gl1 <- runif(100, min=-5, max=5)
gl1 <- gl1[order(gl1, decreasing=TRUE)]
gl2 <- runif(100, min=-5, max=5)
gl2 <- gl2[order(gl2, decreasing=TRUE)]
names(gl1) <- as.character(sample(x=seq(from=1, to=100, by=1), size=100,
replace=FALSE))
names(gl2) <- names(gl1)
gs1 <- sample(names(gl1), size=20, replace=FALSE)
gs2 <- sample(names(gl1), size=20, replace=FALSE)
gsc <- list(subset1=gs1, subset2=gs2)
pwGSEAscore <- pairwiseGsea(gl1=gl1, gl2=gl2, gsc=gsc)