R/preprocess.R

In grafab/pergola: Toolbox for Polyploid Genetic Data

#' Transform bases into genotypes
#'
#' Preprocess the input data in case bases are provided instead of genotypes
#'
#' @param input Matrix of genotype bases. Rows represent the individual markers.
#' Columns represent samples, dependenden on the ploidy.
#' @param ploidy Ploidy level of the organism.
#' Influences how many columns are collapsed into one.
#' @return Matrix of genotypes.
#' The number of columns is 1/\code{ploidy} of the \code{input}.
#' @examples
#' data(simTetra)
#' basesToGenotypes(simTetra, 4)
#' @export
basesToGenotypes <- function(input, ploidy) {
  nGeno <- ncol(input)
  output <-
    apply(input, 1, function(y)
      sapply(seq(1, nGeno, by = ploidy),
             function(x)
               sum(y[x:(x + ploidy - 1)] == y[1])))
  t(output)
}

#' Randomize marker order and alleles within samples
#'
#' In simulated datasets, the order or markers and alleles within samples is often given.
#' To remove any prior knowledge, that would not be available, the data should be randomized.
#' Thus, the performance of our tool can be validated unbiased.
#'
#' @param input Matrix of genotypes. Rows represent markers.
#' Columns represent samples.
#' @param ploidy Ploidy level of the organism. Default is 4.
#' @param ignore In case of unnecessary fronstanding columns (e.g. parental genotypes or rownames), these can be excluded from the randomization.
#' @return Matrix of the same size as the input matrix.
#' The markers are in a random order and the alleles within the samples are in a random order.
#' @examples
#' data(simTetra)
#' shuffleInput(simTetra, 4)
#' @export
shuffleInput <- function(input,
                         ploidy = 4,
                         ignore = 0) {
  numbMark <- nrow(input)
  numbSamp <- (ncol(input) - ignore) / ploidy
  if (numbSamp %% 1 != 0) {
    stop(
      "Number of columns (",
      ncol(input) ,
      ") is not ploidy (",
      ploidy,
      ") * number of samples (",
      numbSamp,
      ")."
    )
  }
  #shuffle rows
  shufMat <- input[sample(1:numbMark, numbMark, replace = FALSE),]
  #shuffle columns individual wise
  if (ploidy > 1) {
    for (i in seq(ignore + 1, ncol(shufMat), by = ploidy)) {
      for (j in 1:numbMark) {
        vec <- i:(i + ploidy - 1)
        shufMat[j, vec] <-
          sample(shufMat[j, vec], ploidy, replace = FALSE)
      }
    }
  }
  return(shufMat)
}