Nothing
CalcPvalWilcox <-
function(counts, treatment, replic = NULL, sort.method, sorted = FALSE, norm.factors, exact = FALSE){
#calculate p-value of differential depression for each gene
#if sort.method == paired, use signed rank test
#if sort.method == unpaired, use ranksum test
if(sorted == FALSE) {
sort.list <- SortData(counts, treatment, replic, sort.method, norm.factors)
counts <- sort.list[[1]]
treatment <- sort.list[[3]]
norm.factors <- sort.list[[4]]
}
n.row <- dim(counts)[1]
n.col <- dim(counts)[2]
probs <- numeric(dim(counts)[1])
if (sort.method == "paired"){
odds <- seq(from = 1, to = n.col, by = 2)
diff1 <- t(counts[, odds, drop = FALSE] + 1)/norm.factors[odds]
diff2 <- t(counts[, (odds + 1), drop = FALSE] + 1)/norm.factors[(odds + 1)]
diff <- log(t(diff1/diff2))
probs <- apply(diff, 1 , function(x) wilcox.test(x, exact = exact)$p.value)
}
if (sort.method == "unpaired"){
seq.trt1 <- 1:table(treatment)[1]
trt1 <- log(t(t(counts[, seq.trt1, drop = FALSE] + 1)/norm.factors[seq.trt1]))
trt2 <- log(t(t(counts[, -seq.trt1, drop = FALSE] + 1)/norm.factors[-seq.trt1]))
probs <- numeric(n.row)
for (jj in 1:n.row) probs[jj] <-
wilcox.test(trt1[jj, ], trt2[jj, ], exact = exact)$p.value
}
return(probs)
}
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