R/callFeatSel.R

In BaderLab/netDx: Network-based patient classifier

#' Return feature selected nets based on given criteria
#'
#' @details given the output of genNetScores.R and criteria for defining
#' feature-selected (FS) nets, returns subset of nets that pass criteria.
#' Net must score <fsCutoff> for at least <fsPctPass> % of splits, to be
#' considered feature-selected.
#' @param netScores (matrix) matrix of net scores
#' @param fsCutoff (integer) net must score at least this much in a split to
#'  'pass' the threshold
#' @param fsPctPass (numeric 0 to 1) net must pass at least this percent of
#' splits to be considered feature-selected
#' @return (char) names of nets that pass feature-selection
#' @examples
#' data(featScores)
#' passed <- lapply(featScores, function(x) {
#'    callFeatSel(x,10,0.7) # score 10/10 in >=70% of trials
#' })
#' print(passed)
#' @export
callFeatSel <- function(netScores, fsCutoff, fsPctPass) {
    fs_nets <- c()
    for (index in seq_len(nrow(netScores))) {
        cur_pathway <- netScores[index, ]
        pass_thresh <- length(which(cur_pathway >= fsCutoff))
        percent_pass <- pass_thresh/length(cur_pathway)
        if (percent_pass >= fsPctPass) {
            fs_nets <- c(fs_nets, netScores[, 1][index])
        }
    }
    return(fs_nets)
}