Compute the GSEA false discovery rates for a collection (list) of gene sets

Description

This function computes the GSEA fdr over a list of gene sets

Usage

1
FDRcollectionGsea(permScores, dataScores)

Arguments

permScores

a numeric matrix of permutation-based scores resulting from the output of collectionGsea

dataScores

a named numeric vector of observed scores resulting from the output of collectionGsea

Value

a named numeric vector of FDR, one for each gene set

Author(s)

Camille Terfve, Xin Wang

References

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.

See Also

collectionGsea, permutationPvalueCollectionGsea

Examples

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##example 1
gl <- runif(100, min=0, max=5)
gl <- gl[order(gl, decreasing=TRUE)]
names(gl) <- as.character(sample(x=seq(from=1, to=100, by=1), size=100,
replace=FALSE))
gs1 <- sample(names(gl), size=20, replace=FALSE)
gs2 <- sample(names(gl), size=20, replace=FALSE)
gscs <- list(gs1=gs1, gs2=gs2)
GSCscores <- collectionGsea(collectionOfGeneSets=gscs, geneList=gl,
exponent=1, nPermutation=1000, minGeneSetSize=5)
GSCfdrs <- FDRcollectionGsea(permScores=GSCscores$Permutation.scores,
dataScores=GSCscores$Observed.scores)
##example 2 (see the vignette for details about the preprocessing of this
##data set)
## Not run: 
library(org.Dm.eg.db)
library(KEGG.db)
data("KcViab_Data4Enrich")
DM_KEGG <- KeggGeneSets(species="Dm")
GSCscores <- collectionGsea(collectionOfGeneSets=DM_KEGG, geneList=
KcViab_Data4Enrich, exponent=1, nPermutations=1000, minGeneSetSize=100)
GSCfdrs <- FDRcollectionGsea(permScores=GSCscores$Permutation.scores,
dataScores=GSCscores$Observed.scores)

## End(Not run)

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