R/IsopvaluePlot.R

In IsoGeneGUI: A graphical user interface to conduct a dose-response analysis of microarray data

`IsopvaluePlot` <-
function (x, y, niter, seed, stat = c("E2", "Williams", "Marcus", 
    "M", "ModifM")) 
{
    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
    set.seed(seed)
    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 = ""))
}