Nothing

```
##This function computes enrichment scores for both input 'geneList'
##and their permutations for multiple gene sets in parallel
gseaScoresBatchParallel <- function(geneList, geneNames.perm,
collectionOfGeneSets, exponent = 1, nPermutations = 1000) {
##check arguments
paraCheck("genelist",geneList)
paraCheck("gsc",collectionOfGeneSets)
paraCheck("exponent",exponent)
paraCheck("nPermutations",nPermutations)
if(!is.matrix(geneNames.perm))
stop("'geneNames.perm' should be a matrix!\n")
if(ncol(geneNames.perm)!=(nPermutations+1))
stop("The No of columns of 'geneNames.perm' should be equal to 'nPermutations'!\n")
##local function for computation of gsea scores with a single core
gseaScoresBatchLocal <- function(geneList, geneNames.perm, geneSet,
exponent, nPermutations) {
geneList.names <- names(geneList)
##The geneSet should be a subset of the gene universe, i.e. we
##keep only those element of the gene set that appear in the
##geneList
geneSet <- intersect(geneList.names, geneSet)
##Compute the size of the gene set and of the genelist
nh <- length(geneSet)
N <- length(geneList)
ES <- rep(0, nPermutations+1)
Phit <- matrix(0, nrow = N, ncol = nPermutations+1)
Pmiss <- Phit
runningES <- NULL
if(nh > N)
stop("Gene Set is larger than Gene List")
hits <- matrix(FALSE, nrow = N, ncol = nPermutations+1)
hits[which(!is.na(match(geneNames.perm, geneSet)))] <- TRUE
hits <- matrix(hits, ncol = nPermutations+1, byrow = FALSE)
if(sum(hits[,1]) > 0) {
junk <- sapply(1:(nPermutations+1), function(i)
Phit[which(hits[, i]), i] <<-
abs(geneList[which(hits[, i])])^exponent)
NR <- colSums(Phit)
Pmiss[which(!hits)] <- 1/(N-nh)
Pmiss <- sapply(1:(nPermutations+1), function(i)
cumsum(Pmiss[, i]))
Phit <- sapply(1:(nPermutations+1), function(i)
cumsum(Phit[, i])/NR[i])
runningES <- Phit-Pmiss
ESrange <- sapply(1:(nPermutations+1), function(i)
range(runningES[, i]))
ES <- sapply(1:(nPermutations+1), function(i)
ESrange[which.max(abs(ESrange[,i])),i])
if(is.list(ES)) ES <- unlist(ES)
}
##Return the relevant information according to mode
ES <- list(scoresObserved = ES[1], scoresperm = ES[2:(nPermutations+1)])
return(ES)
}
#parallel computing
scores <- parSapply(getOption("cluster"), 1:length(collectionOfGeneSets),
function(i) {
gseaScoresBatchLocal(geneList, geneNames.perm = geneNames.perm,
geneSet = as.integer(collectionOfGeneSets[[i]]), exponent = exponent,
nPermutations = nPermutations)
}
)
return(scores)
}
```

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