Nothing
IsopvaluePlot <- function(x, y, niter, stat=c("E2","Williams","Marcus","M","ModifM") ){
# TODO: add check on niter
Probe.ID <- row.names(y)
obs <- IsoGene1(x,y)
stat <- match.arg(stat)
obs.up <- switch(stat,
E2 = obs[[1]],
Williams = obs[[2]],
Marcus = obs[[3]],
M = obs[[4]],
ModifM = obs[[5]])
obs.dn <- switch(stat,
E2 = obs[[6]],
Williams = obs[[7]],
Marcus = obs[[8]],
M = obs[[9]],
ModifM = obs[[10]])
exp.up <- exp.dn <- 1:niter
### replicate ?
x.niter <- t(sapply(1:niter, function(i) sample(x)))
for (j in 1:niter){
exps <- IsoGene1(x.niter[j,], y)
exp.up[j] <- switch(stat,
E2 = exps[[1]],
Williams = exps[[2]],
Marcus = exps[[3]],
M = exps[[4]],
ModifM = exps[[5]])
exp.dn[j] <- switch(stat,
E2 = exps[[6]],
Williams = exps[[7]],
Marcus = exps[[8]],
M = exps[[9]],
ModifM = exps[[10]])
cat(paste(j, ". "))
}
rawp.up <- sum(obs.up < exp.up) / niter
rawp.dn <- sum(obs.dn > exp.dn) / niter
if (stat == "E2") rawp.dn <- sum(obs.dn < exp.dn) / niter
par(mfrow = c(2,1))
hist(exp.up, main = "", nclass = 1000, col = 0, probability = TRUE,
xlim = c(min(exp.up, obs.up), max(exp.up, obs.up)), xlab = paste(stat))
dx <- density(exp.up, from = min(exp.up), to = max(exp.up))
abline(v = obs.up, col = 7, lwd = 3)
lines(dx$x,dx$y, lwd = 3, col = 5)
title(paste("Gene: ", Probe.ID, ":p-value^{up}=", rawp.up, sep=""))
hist(exp.dn, main = "", nclass = 1000, col = 0, probability = TRUE,
xlim = c(min(exp.dn,obs.dn), max(exp.dn,obs.dn)), xlab = paste(stat))
dx <- density(exp.dn, from = min(exp.dn), to = max(exp.dn))
abline(v = obs.dn, col = 7, lwd = 3)
lines(dx$x, dx$y, lwd = 3, col = 5)
title(paste("Gene: ", Probe.ID, ":p-value^{down}=", rawp.dn, sep = ''))
}
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