Nothing

```
##This function computes the FDR associated with a permutation-based
##p-value from the GSEA on a list of gene sets
FDRcollectionGsea <- function(permScores, dataScores){
##check arguments
if(!is.matrix(permScores))
stop("'permScores' should be a matrix!\n")
if(!is.numeric(dataScores) && !is.integer(dataScores))
stop("'dataScores' should be an integer or numeric vector!\n")
if(is.null(names(dataScores)))
stop("'dataScores' should be named (by gene set identifier)")
if(nrow(permScores) != length(dataScores))
stop(paste("The number of rows of the 'permScores' matrix ",
"should be the same as the length of the 'dataScores' vector",
sep=""))
##create a vector to store the FDRs
ldataScores<-length(dataScores)
FDRgeneset=rep(0,ldataScores)
##Compute the normalized enrichment score (i.e. divide each ES
##(experimental or permutation-based) by the mean of all negative/positive
##(depending on the sign of the ES) permutation based scores for that gene set)
##This is done gene set by gene set
sapply(1:ldataScores, function(i) {
##Get the indices of all negative permutation-based score
neg<-which(permScores[i,] <= 0)
##Get the indices of all positive permutation-based score
pos<-which(permScores[i,] >= 0)
##Average the values, separately for positive and negative scores
NegAvg<-abs(mean(permScores[i,neg]))
PosAvg<-abs(mean(permScores[i,pos]))
##Normalize the permutation-based scores,
##separately for negative and positive scores
permScores[i,neg]<<-permScores[i,neg]/NegAvg
permScores[i,pos]<<-permScores[i,pos]/PosAvg
##Normalize the observed scores, separately for negative and positive scores
dataScores[i] <<- ifelse((dataScores[i] < 0),
(dataScores[i]/NegAvg), (dataScores[i]/PosAvg))
})
##Compute the total number of negative/positive scores across all
##permutations and all gene sets
negtot <- length(which(permScores <= 0))
postot <- length(which(permScores >= 0))
##Compute the FDR by comparing:
##for negative observed ES:
##the number of permutation-based scores under the observed ES
##divided by the total number of negative permutation based scores
##to the number of observed scores under the observed ES divided
##by the total number of negative observed scores
##for positive observed ES:
##the number of permutation-based scores over the observed ES
##divided by the total number of positive permutation based scores
##to the number of observed scores over the observed ES divided
##by the total number of positive observed scores
sapply(1:ldataScores, function(i) {
if(is.na(dataScores[i])) {
FDRgeneset[i] <<- 1
} else if(dataScores[i] < 0) {
FDRgeneset[i] <<- (sum(permScores <= dataScores[i])/negtot)/
(sum(dataScores <= dataScores[i])/sum(dataScores <= 0))
} else {
FDRgeneset[i] <<- (sum(permScores >= dataScores[i])/postot)/
(sum(dataScores >= dataScores[i])/sum(dataScores >= 0))
}
FDRgeneset[i] <<- ifelse(FDRgeneset[i]>1, 1, FDRgeneset[i])
})
#name the FDRs (by gene set name) and return the vector
names(FDRgeneset) <- names(dataScores)
return(FDRgeneset)
}
```

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