R/ti_monocle_ica.R

In dynverse/dynmethods: A collection of trajectory inference methods

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#' @title Monocle ICA
#' 
#' @description
#' Will generate a trajectory using [Monocle
#' ICA](https://doi.org/10.1038/nmeth.4402).
#' 
#' This method was wrapped inside a
#' [container](https://github.com/dynverse/ti_monocle_ica).
#' The original code of this method is available
#' [here](https://github.com/cole-trapnell-lab/monocle-release).
#' 
#' @references Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H.A.,
#' Trapnell, C., 2017. Reversed graph embedding resolves complex single-cell
#' trajectories. Nature Methods 14, 979–982.
#' 
#' @param reduction_method A character string specifying the algorithm to use for
#' dimensionality reduction. Domain: {ICA}. Default: ICA. Format: character.
#' @param max_components The dimensionality of the reduced space. Domain: U(2,
#' 20). Default: 2. Format: integer.
#' @param norm_method Determines how to transform expression values prior to
#' reducing dimensionality. Domain: {vstExprs, log, none}. Default: log. Format:
#' character.
#' @param filter_features Whether to include monocle feature filtering. Default:
#' TRUE. Format: logical.
#' @param filter_features_mean_expression Minimal mean feature expression, only
#' used when `filter_features` is set to TRUE. Domain: U(0, 10). Default: 0.1.
#' Format: numeric.
#' 
#' @keywords method
#' 
#' @return A TI method wrapper to be used together with
#' \code{\link[dynwrap:infer_trajectories]{infer_trajectory}}
#' @export
ti_monocle_ica <- function(
    reduction_method = "ICA",
    max_components = 2L,
    norm_method = "log",
    filter_features = TRUE,
    filter_features_mean_expression = 0.1
) {
  method_choose_backend(
    package_repository = NULL,
    package_name = NULL,
    function_name = NULL,
    package_version = NULL,
    container_id = "dynverse/ti_monocle_ica:v0.9.9.01"
  )(
    reduction_method = reduction_method,
    max_components = max_components,
    norm_method = norm_method,
    filter_features = filter_features,
    filter_features_mean_expression = filter_features_mean_expression
  )
}