test_ssgsea <- function() {
message("Running unit tests for ssGSEA.")
p <- 10 ## number of genes
n <- 30 ## number of samples
nGrp1 <- 15 ## number of samples in group 1
nGrp2 <- n - nGrp1 ## number of samples in group 2
## consider three disjoint gene sets
geneSets <- list(set1=paste("g", 1:3, sep=""),
set2=paste("g", 4:6, sep=""),
set3=paste("g", 7:10, sep=""))
## sample data from a normal distribution with mean 0 and st.dev. 1
## seeding the random number generator for the purpose of this test
set.seed(123)
y <- matrix(rnorm(n*p), nrow=p, ncol=n,
dimnames=list(paste("g", 1:p, sep="") , paste("s", 1:n, sep="")))
## genes in set1 are expressed at higher levels in the last 'nGrp1+1' to 'n' samples
y[geneSets$set1, (nGrp1+1):n] <- y[geneSets$set1, (nGrp1+1):n] + 2
## estimate GSVA enrichment scores for the three sets
es <- gsva(ssgseaParam(y, geneSets), verbose=FALSE)
checkTrue(max(abs(rowMeans(es) - c(0.22893323, -0.04400744, -0.08289233))) < 1e-08)
checkTrue(max(abs(apply(es, 1,sd) - c(0.2562903, 0.2260589, 0.2268853))) < 1e-07)
gset.idx.list <- lapply(geneSets,
function(x, y) na.omit(match(x, y)),
rownames(y))
fast.gset.idx.list <- lapply(geneSets,
function(x, y) na.omit(match(x, y)),
rownames(y))
checkIdentical(gset.idx.list, fast.gset.idx.list)
R <- apply(y, 2, function(x ,p) as.integer(rank(x)), p)
alpha <- 0.25
Ra <- abs(R)^alpha
for (i in 1:n) {
geneRanking <- order(R[, i], decreasing=TRUE)
gset1rnkidx <- match(gset.idx.list[[1]], geneRanking)
frw <- GSVA:::.fastRndWalk(gset1rnkidx, geneRanking, i, Ra)
rw <- GSVA:::.rndWalk(gset.idx.list[[1]], geneRanking, i, R, alpha)
checkEqualsNumeric(rw, frw)
}
}
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