Nothing

```
##This function computes observed and permutation-based scores associated
##with a gene set enrichment analysis for a collection of Gene Sets.
collectionGsea <- function(collectionOfGeneSets, geneList, exponent=1,
nPermutations=1000, minGeneSetSize=15, verbose=TRUE) {
##check input arguments
paraCheck("gsc", collectionOfGeneSets)
paraCheck("genelist", geneList)
paraCheck("exponent", exponent)
paraCheck("minGeneSetSize", minGeneSetSize)
geneList.names <- names(geneList)
paraCheck("nPermutations", nPermutations)
##tag the gene sets that can be used in the analysis, i.e. those
##that are smaller than the size of the gene list and that have more
##than 'minGeneSetSize' elements that can be found in the geneList
nGeneSets <- length(collectionOfGeneSets)
tagGeneSets <- rep(FALSE, nGeneSets)
tagGeneSets[which(unlist(lapply(collectionOfGeneSets, length)) <
length(geneList))] <- TRUE
tagGeneSets[which(unlist(lapply(lapply(collectionOfGeneSets,
intersect, y=geneList.names), length)) < minGeneSetSize)] <- FALSE
##check that there are actually some gene sets that pass the max
##and min cutoffs
n.tagGeneSets <- sum(tagGeneSets)
if(n.tagGeneSets == 0)
warning(paste("There are no gene sets in your collection",
" that pass the cutoffs on size", sep=""))
if(n.tagGeneSets > 0) {
##Generate a matrix to store the permutation-based scores, with
##one row for each gene set (that has been tagged) and one column
##for each permutation
scoresperm <- matrix(rep(0, (nPermutations * n.tagGeneSets)),
nrow=n.tagGeneSets)
rownames(scoresperm) <- names(collectionOfGeneSets)[which(tagGeneSets)]
##Generate a vector to store the experimental scores
##one entry for each gene set (that has been tagged)
scoresObserved <- rep(0, n.tagGeneSets)
names(scoresObserved) <- names(collectionOfGeneSets)[which(tagGeneSets)]
##Compute the scores
##create permutation gene list
perm.gL <- sapply(1:nPermutations, function(n) names(geneList)[
sample(1:length(geneList), length(geneList),replace=FALSE)])
perm.gL<-cbind(names(geneList),perm.gL)
##check if package snow has been loaded and a cluster object
##has been created for HTSanalyzeR
if(is(getOption("cluster"), "cluster") &&
"package:snow" %in% search()) {
scores <- gseaScoresBatchParallel(geneList, geneNames.perm = perm.gL,
collectionOfGeneSets=collectionOfGeneSets[which(tagGeneSets)],
exponent=exponent,nPermutations=nPermutations)
sapply(1:n.tagGeneSets, function(i) {
scoresperm[i,]<<-unlist(scores["scoresperm",i])
scoresObserved[i]<<-unlist(scores["scoresObserved",i])
}
)
} else {
if(verbose)
pb <- txtProgressBar(style=3)
for(i in 1:n.tagGeneSets) {
scores <- gseaScoresBatch(geneList, geneNames.perm=perm.gL,
geneSet=collectionOfGeneSets[[which(tagGeneSets)[i]]],
exponent=exponent, nPermutations=nPermutations)
scoresObserved[i] <- scores$scoresObserved
scoresperm[i,] <- scores$scoresperm
if(verbose)
setTxtProgressBar(pb, i/n.tagGeneSets)
}
if(verbose)
close(pb)
}
} else {
scoresObserved <- NULL
scoresperm <- NULL
}
return(list("Observed.scores" = scoresObserved , "Permutation.scores" = scoresperm))
}
```

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