collectionGsea: Compute observed and permutation-based enrichment scores for...

In HTSanalyzeR: Gene set over-representation, enrichment and network analyses for high-throughput screens

Description

This function computes observed and permutation-based scores associated with a gene set enrichment analysis for a collection of gene sets.

Usage

collectionGsea(collectionOfGeneSets, geneList, exponent=1, nPermutations=
1000, minGeneSetSize=15, verbose=TRUE)

Arguments

collectionOfGeneSets	a list of gene sets. Each gene set in the list is a character vector of gene identifiers.
geneList	a numeric or integer vector which has been named and ordered. It cannot contain any duplicates nor NAs.
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.
verbose	a single logical value specifying to display detailed messages (when verbose=TRUE) or not (when verbose=FALSE)

Value

Observed.scores	The observed scores for the given gene sets (a named vector)
Permutation.scores	The scores for the permutation tests (one column for each permutation and a row 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

FDRcollectionGsea

Examples

##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)
gsc <- list(subset1=gs1, subset2=gs2)
GSCscores <- collectionGsea(collectionOfGeneSets=gsc, geneList=gl,
exponent=1, nPermutations=1000, minGeneSetSize=5)
GSCpvalues <- permutationPvalueCollectionGsea(permScores=
GSCscores$Permutation.scores, dataScores=GSCscores$Observed.scores)
##example 2
## Not run: 
library(org.Dm.eg.db)
library(KEGG.db)
##load phenotype vector (see the vignette for details about the
##preprocessing of this data set)
data("KcViab_Data4Enrich")
DM_KEGG <- KeggGeneSets(species="Dm")
GSCscores <- collectionGsea(collectionOfGeneSets=DM_KEGG, geneList=
KcViab_Data4Enrich, exponent=1, nPermutations=1000, minGeneSetSize=100)
GSCpvalues <- permutationPvalueCollectionGsea(permScores=
GSCscores$Permutation.scores, dataScores=GSCscores$Observed.scores)

## End(Not run)

HTSanalyzeR documentation built on Oct. 31, 2019, 7:10 a.m.