R/RleDataFrame-methods.R

In genoset: A RangedSummarizedExperiment with methods for copy number analysis

##' @include RleDataFrame-class.R
NULL

##' @exportMethod colMeans
setMethod("colMeans", "RleDataFrame", function(x, na.rm = TRUE) {
    mean(x, na.rm = na.rm)
})

setMethod("colMeans", signature(x = "DataFrame"), function(x, na.rm = TRUE, dims = 1L) {
    .Deprecated("colMeans", msg = "colMeans on a DataFrame is Deprecated. It is kind of odd given that the column type are arbitrary. Try RleDataFrame, or another class that inherits from AtomicList and DataFrame. But, if you find this DataFrame version useful, let me know.")
    return(vapply(x, mean, na.rm = na.rm, FUN.VALUE = numeric(1), USE.NAMES = TRUE))
})

##' @exportMethod colSums
setMethod("colSums", "RleDataFrame", function(x, na.rm = TRUE) {
    sum(x, na.rm = na.rm)
})
##' @exportMethod rowMeans
setMethod("rowMeans", signature(x = "RleDataFrame"), function(x, na.rm = FALSE, dims = 1L) {
    if (na.rm == TRUE) {
        sums = x[[1L]]
        na.sums = is.na(runValue(sums))
        runValue(sums)[na.sums] = 0
        na.count = Rle(na.sums, runLength(sums))
        for (i in 2L:ncol(x)) {
            current = x[[i]]
            is.na.current = is.na(runValue(current))
            na.current = Rle(is.na.current, runLength(current))
            runValue(current)[is.na.current] = 0
            sums = sums + current
            na.count = na.count + na.current
        }
        means = sums/(ncol(x) - na.count)
    } else {
        sums = x[[1L]]
        for (i in 2L:ncol(x)) {
            sums = sums + x[[i]]
        }
        means = sums/ncol(x)
    }
    return(means)
})

##' @exportMethod rowSums
setMethod("rowSums", signature(x = "RleDataFrame"), function(x, na.rm = FALSE, dims = 1L) {
    if (na.rm == TRUE) {
        sums = x[[1L]]
        runValue(sums)[is.na(runValue(sums))] = 0
        for (i in 2L:ncol(x)) {
            current = x[[i]]
            runValue(current)[is.na(runValue(current))] = 0
            sums = sums + current
        }
    } else {
        sums = x[[1L]]
        for (i in 2L:ncol(x)) {
            sums = sums + x[[i]]
        }
    }
    return(sums)
})