#' Re-sample real dataset parameters.
#' Re-sample parameters extracted from real dataset, to be used for the simulation dataset.
#' @param object DGEList object.
#' @param seed Optional seed, if it desired to replicate the simulation.
#' @keywords internal
#' @export
#' @examples
#'
#' @return Saves re-sampled gene-wise lambdas and dispersions to DGEList object.
#'
sample.fun <- function(object, seed)
{
nlibs <- object$nlibs
nTags <- object$nTags
# Parameters extracted from real dataset
aveLogCPM <- object$dataset.params$aveLogCPM
dispersion <- object$dataset.params$dispersion
pZero <- object$dataset.params$pZero
# Indices from sampling a subset of those real dataset parameters
set.seed(seed)
id_r <- sample(length(aveLogCPM), nTags, replace = TRUE)
# mu, dispersion = genewise parameters to be used for simulation dataset
# mu is the baseline rate per gene
mu <- 2^(aveLogCPM[id_r]) #lambda for each gene
mu <- mu/sum(mu)
dispersion <- dispersion[id_r]
pZero <- pZero[id_r]
# Get ids for which gene-wise rates are 0
id_0 <- mu == 0
# Keep only the parameters for which lambdas are not 0
mu <- mu[!id_0]
dispersion <- dispersion[!id_0]
pZero <- pZero[!id_0]
object$mu <- mu
object$dispersion <- dispersion
object$pZero <- pZero
object
}
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