Nothing
KS.score <-
function(expr.data, gene.sets){
ord <- order(expr.data,decreasing=FALSE)
expr.data <- expr.data[ord]
gene.sets <- gene.sets[ord,]
set.dim <- dim(gene.sets)
result1 <- rep(0,set.dim[2])
result2 <- result1
profile2 <- matrix(0,set.dim[2],set.dim[1])
expr.data.len <- length(expr.data)
for(i in 1:set.dim[2]){
po1 <- which(gene.sets[,i]==1)
po0 <- which(gene.sets[,i]==0)
tmp1 <- expr.data
tmp1[po0] <- 0
tmp0 <- expr.data
tmp0[po1] <- 0
tmp2 <- cumsum(abs(tmp1))/sum(abs(tmp1))
tmp2b <- tmp1
tmp2b[po1] <- 1
tmp2c <- cumsum(tmp2b)/sum(tmp2b)
tmp0b <- tmp0
tmp0b[po0] <- 1
tmp3 <- cumsum(abs(tmp0))/sum(abs(tmp0))
tmp3b <- cumsum(abs(tmp0b))/sum(abs(tmp0b))
result1[i] = max(abs(tmp2c - tmp3b))
result2[i] = max(abs(tmp2 - tmp3b))
#profile2.abs <- abs(tmp2 - tmp3b)
#profile1.abs <- abs(tmp2c - tmp3b)
#profile2[i,] <- tmp2 - tmp3b
# profile1 <- tmp2c - tmp3b
}
out <- list(KS.org = result1,KS.mod = result2)
}
#############
WRS.test.score <-
function(expr.data, gene.sets){
ord <- order(expr.data)
expr.data <- expr.data[ord]
rnk <- rank(expr.data)
gene.sets <- gene.sets[ord,]
num.classes <- dim(gene.sets)[2]
WRS.score <- rep(0,num.classes)
for(i in 1:num.classes){
pos1 <- which(gene.sets[,i]==1)
num.cl.members <- length(pos1)
pos0 <- which(gene.sets[,i]==0)
num.non.cl.members <- length(pos0)
R.cl.members <- sum(rnk[pos1])
##sum of ranks of non-class members
R.non.cl.members <- sum(rnk[pos0])
WRS.score[i] <- R.cl.members
}
return(WRS.score)
}
##############
SS.test.score <-
function(expr.data, gene.sets){
num.classes <- dim(gene.sets)[2]
Mu <- mean(expr.data)
SS.score = rep(0,num.classes)
for(i in 1:num.classes){
pos1 <- which(gene.sets[,i]==1)
##Calculation of sum of squares
SS.score[i] <- sum((expr.data[pos1]-Mu)^2)
}
return(SS.score)
}
################
sum.test.score <-
function(expr.data, gene.sets){
num.classes <- dim(gene.sets)[2]
Mu <- mean(expr.data)
sum.score = rep(0,num.classes)
for(i in 1:num.classes){
pos1 <- which(gene.sets[,i]==1)
##Calculation of sum of squares
sum.score[i] <- sum(expr.data[pos1]-Mu)
}
return(sum.score)
}
##################
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