R/mfa-estimate.R

In Granoia/splatter-mod: Simple Simulation of Single-cell RNA Sequencing Data

#' Estimate mfa simulation parameters
#'
#' Estimate simulation parameters for the mfa simulation from a real dataset.
#'
#' @param counts either a counts matrix or a SingleCellExperiment object
#'        containing count data to estimate parameters from.
#' @param params MFAParams object to store estimated values in.
#'
#' @details
#' The \code{nGenes} and \code{nCells} parameters are taken from the size of the
#' input data. The dropout lambda parameter is estimate using
#' \code{\link[mfa]{empirical_lambda}}. See \code{\link{MFAParams}} for more
#' details on the parameters.
#'
#' @return MFAParams object containing the estimated parameters.
#'
#' @examples
#' # Load example data
#' library(scater)
#' data("sc_example_counts")
#'
#' params <- mfaEstimate(sc_example_counts)
#' params
#' @export
mfaEstimate <- function(counts, params = newMFAParams()) {
    UseMethod("mfaEstimate")
}

#' @rdname mfaEstimate
#' @export
mfaEstimate.SingleCellExperiment <- function(counts,
                                             params = newMFAParams()) {
    counts <- BiocGenerics::counts(counts)
    mfaEstimate(counts, params)
}

#' @rdname mfaEstimate
#' @export
mfaEstimate.matrix <- function(counts, params = newMFAParams()) {

    checkmate::assertClass(params, "MFAParams")

    dropout.lambda <- mfa::empirical_lambda(t(counts))

    params <- setParams(params, nGenes = nrow(counts), nCells = ncol(counts),
                        dropout.lambda = dropout.lambda)

    return(params)
}