R/gaphunter.R

In minfi: Analyze Illumina Infinium DNA methylation arrays

gaphunter <- function(object, threshold = 0.05, keepOutliers = FALSE,
                      outCutoff = 0.01, verbose = TRUE) {
    # Check inputs
    if ((threshold <= 0) || (threshold >= 1)) {
        stop("[gaphunter] 'threshold' must be between 0 and 1.")
    }
    if ((outCutoff <= 0) || (outCutoff >= 0.5)) {
        stop("[gaphunter] 'outCutoff' must be between 0 and 0.5.")
    }
    if (is(object, "GenomicRatioSet") || is(object, "GenomicMethylSet") ||
        is(object, "MethylSet") || is(object,"RatioSet")) {
        .isMatrixBackedOrStop(object, "gaphunter")
        if (verbose) message("[gaphunter] Calculating beta matrix.")
        Beta <- getBeta(object)
    } else {
        if (is(object, "matrix")) {
            test <- rowRanges(object)
            if (sum(test[, 1] < 0, na.rm = TRUE) == 0 &&
                sum(test[, 2] > 1, na.rm = TRUE) == 0) {
                Beta <- object
            } else {
                stop("[gaphunter] Matrix must be of Beta values with range ",
                     "from 0 to 1") }
        } else {
            stop("[gaphunter] Object must be one of (Genomic)RatioSet, ",
                 "(Genomic)MethylSet, or matrix")
        }
    }
    nacheck <- rowCounts(Beta, value = NA_real_)
    if (sum(nacheck > 0) > 0) {
        if (verbose) {
            message("[gaphunter] Removing probes containing missing beta ",
                    "values.")
        }
        Beta <- Beta[which(nacheck == 0),]
    }

    if (verbose) {
        message(
            "[gaphunter] Using ",
            prettyNum(nrow(Beta), big.mark = ",", scientific = FALSE),
            " probes and ",
            prettyNum(ncol(Beta), big.mark = ",", scientific = FALSE),
            " samples.")
        message("[gaphunter] Searching for gap signals.")
    }

    sorting <- t(
        apply(Beta, 1, sort.int, method = "quick", index.return = TRUE))
    sortedindices <- lapply(sorting, function(n) n$ix)
    sortedbeta <- do.call("rbind", lapply(sorting, function(n) n$x))
    rownames(sortedbeta) <- rownames(Beta)
    diffs <- rowDiffs(sortedbeta)
    gapind <- rowSums2(diffs > threshold)
    sortedindices <- lapply(which(gapind > 0), function(h) sortedindices[[h]])
    sortedbeta <- sortedbeta[which(gapind > 0),]
    diffs <- diffs[which(gapind > 0),]
    gapind <- gapind[which(gapind > 0)]

    breakpoints <- apply(diffs, 1, function(x) which(x > threshold))

    returngroups <- function(x, y) {
        template <- rep(1, ncol(sortedbeta))
        count <- 2
        for (j in x) {
            template[y[seq(j + 1L, length(y))]] <- count
            count <- count + 1
        }
        template
    }

    groupanno <- t(mapply(returngroups, breakpoints, sortedindices))
    maxGroups <- max(groupanno)
    gapanno <- matrix(0, nrow = nrow(groupanno), ncol = maxGroups + 1)
    gapanno[,1] <- rowMaxs(groupanno)
    for (ii in 1:maxGroups) {
        gapanno[, ii + 1] <- rowCounts(groupanno, value = ii)
    }
    # TODO: Remove commented chunk if not needed
    ## gapanno <- apply(groupanno, 1, function(k) {
    ##     tempgap <- rep(0, max(gapind) + 2)
    ##     kl <- length(unique(k))
    ##     tempgap[1] <- kl
    ##     tempgap[1 + 1:kl] <- table(k)
    ##     return(tempgap)
    ## })
    ## gapanno <- t(gapanno)
    rownames(gapanno) <- rownames(sortedbeta)
    rownames(groupanno) <- rownames(sortedbeta)

    if (verbose) {
        message(
            "[gaphunter] Found ",
            prettyNum(nrow(gapanno), big.mark = ",", scientific = FALSE),
            " gap signals.")
    }

    if (keepOutliers == FALSE) {
        if (verbose) {
            message("[gaphunter] Filtering out gap signals driven by outliers.")
        }
        markme <- unlist(lapply(seq_len(nrow(gapanno)), function(blah) {
            analyze <- gapanno[blah, -1]
            maxgroup <- which(analyze == max(analyze, na.rm = TRUE))
            if (length(maxgroup) == 1) {
                if (sum(analyze[-maxgroup], na.rm = TRUE) <
                    outCutoff * ncol(Beta)) {
                    return(blah)
                }
            }
        }))
        if (length(markme) > 0) {
            gapanno <- gapanno[-markme, ]
            groupanno <- groupanno[-markme, ]
        }
        if (verbose) {
            message(
                "[gaphunter] Removed ",
                prettyNum(length(markme), big.mark = ",", scientific = FALSE),
                " gap signals driven by outliers from results.")
        }
    }
    gapanno <- data.frame(gapanno)

    colnames(gapanno) <- c("Groups", paste0("Group",seq_len(ncol(gapanno) - 1)))

    algorithm <- list(
        threshold = threshold,
        outCutoff = outCutoff,
        keepOutliers = keepOutliers)

    list(
        proberesults = gapanno,
        sampleresults = groupanno,
        algorithm = algorithm)
}