R/get.probeset.hitchip.R

In RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

#' @title Get probeset
#' @description Get probeset matrix.
#' @param name name
#' @param level taxonomic level
#' @param taxonomy taxonomy
#' @param probedata oligos vs. samples preprocessed data matrix; 
#'                  absolute scale
#' @param log10 Logical. Logarithmize the data TRUE/FALSE
#' @return probeset data matrix
#' @export
#' @examples
#'   #taxonomy <- GetPhylogeny('HITChip', 'filtered')
#'   #data.dir <- system.file("extdata", package = "microbiome")
#'   #probedata <- read_hitchip(data.dir, "rpa")$probedata
#'   #ps <- get.probeset('Akkermansia', 'L2', taxonomy, probedata)
#' @references See citation('microbiome') 
#' @author Contact: Leo Lahti \email{microbiome-admin@@googlegroups.com}
#' @keywords utilities
get.probeset <- function(name, level, taxonomy, probedata, log10 = TRUE) {
    
    taxonomy <- as.data.frame(taxonomy)

    # Pick probes for this entity
    probes <- retrieve.probesets(taxonomy, level, name)
    
    sets <- vector(length = length(probes), mode = "list")
    names(sets) <- names(probes)
    
    for (nam in names(probes)) {
        
        # Pick expression for particular probes (absolute scale)
        p <- intersect(probes[[nam]], rownames(probedata))
        dat <- NULL
        if (length(p) > 0) {
            dat <- probedata[p, ]  
            # Logarithmize probeset?
            if (log10) {
                dat <- log10(dat)
            }
        }
        sets[[nam]] <- dat
        
    }
    
    if (length(sets) == 1) {
        sets <- sets[[1]]
    }
    
    # Return
    sets
    
}