R/fdr.int.R

In OLIN: Optimized local intensity-dependent normalisation of two-color microarrays

fdr.int <- function (A, M, delta = 50, N = 100, av = "median") 
{
    XavP <- double(length(M) * N)

    ### GENERATING BACKGROUND DISTRIBUTION 
    for (i in 1:N) {
        XavP[((i - 1) * length(M) + 1):(i * length(M))] <- ma.vector(A, 
            sample(M), av = av, delta = delta)
    }
    XavP <- XavP[!is.na(XavP)]
    XavP.l <- length(XavP)

    ### STATISTICS OF ORIGINAL DATA 
    Xav <- ma.vector(A, M, av = av, delta = delta)

    ### COMPARING STATISTICS OF ORIGINAL DATA AND PERMUTATED DATA 
    o <- 1:length(Xav)
    ro <- o[rank(Xav)]
    XavS <- sort(Xav)
    XavS.l <- length(XavS)
    XN <- double(length = length(XavS)) + NA
    for (i in 1:XavS.l) {
        XN[i] <- sum(XavP >= XavS[i], na.rm = TRUE)
    }
    XN <- XN/(XavP.l/XavS.l)

    ### CALCULATION OF FALSE POSITIVES RATES
    pFDR <- double(length = length(Xav)) + NA
    for (i in (delta + 1):XavS.l) {
        pFDR[XavS.l - i + 1] <- XN[XavS.l - i + 1]/(XN[XavS.l - 
            i + 1] + i)
    }
    pFDR[pFDR == 0] <- 1/(XavS.l * N)
    nFDR <- double(length = length(Xav)) + NA
    for (i in 1:XavS.l) {
        nFDR[i] <- (XavS.l - XN[i])/((XavS.l - XN[i]) + i)
    }
    nFDR[nFDR == 0] <- 1/(XavS.l * N)
    list(FDRp = pFDR[ro], FDRn = nFDR[ro])
}
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