R/estimateCellTypeProportions.R

In libcell/deconvBench: A tool for approaches evaluation of cell fraction estimation

#' estimateCellTypeProportions
#' Deconvolution methods for large functions
#' @param method Select a method from 37 software for estimating the proportion of cellular
#' fractions in mixed samples.they are "Abis","Adapts","Autogenes","Bayice","Bisquerna","Bseqsc",
#' Cdseq_no","Cdseq","Celldistinguisher","Cibersort","Consensustme","Dcq","Debcam","Demixt","Dtangle",
#' "Dtd","Dwls","Enigma","Epic","Estimate","Fardeep","Mcpcounter","Mixture","Momf","Music","Nitumid",
#' "Prede","Quantiseq","Scbiocpm","Scdc","Singscore","Ssgsea","Ticpe","Timer","Toast","Toast_no",
#' Xcell".
#' @param bulkdata A data.frame (nGenes x nSamples) of the genes expression from each bulk sample.
#' This matrix needs to have rownames telling the gene names (corresponds to the gene symbol in the
#'  prebuilt reference profiles (e.g. CD8A, MS4A1) It is advised to keep all genes in the bulk instead
#'   of a subset of signature genes.
#' @param signature A data frame representing immune cell expression profiles. Each row represents
#'  an expression of a gene, and each column represents a different  cell type. colnames
#'  contains the name of each immune cell type and the rownames includes the genes' symbol.
#'  The names of each immune cell type and the symbol of each gene should be unique.
#' @param scdata  A data frame with unnormalized data with cells as columns and genes as rows.
#' @param label A cell type label of length equal to the number of columns of the scdata, with
#'  each label corresponding to a column of the scdata in turn.
#' @param celltypenum For some reference-free methods, it is necessary to set the number of
#' cell types contained in the mixed sample.
#' @param tissue The tissues of  bulk sample, One of "BLOOD","Gedit","LIVER","LUNG","MUSCLE","OVARY","HEART","BONE",
#'  "ESOPHAGUS","STOMACH","GASTRIC","PANCREAS","CEREBELLUM", "NEUROTYPES","RETINA","EYE","THYMUS","INTESTINE",
#'  "ADRENAL","ESOPHAGEAL","DUODENAL","OLFACTORY","CEREBRUM".
#' @param tumortype 23 cancer types from The Cancer Genome Atlas (TCGA).
#' @param SubMethod Subclass methods for cellular component estimation method software. Such as "NNLS".
#' @param use_docker Whether to use docker images, TRUE or FALSE.
#' @param seed the random seed
#' @param  reftype  defalt is NULL
#'
#' @importFrom dplyr lst
#' @importFrom BiocGenerics do.call
#' @return  A data frame of Mixed cellular fractions.
#' @export
#'
#' @examples
#'
#'
#' # Bulk <- Bulk_GSE60424
#' # SC <- SC_GSE60424
#' # Label <- Label_GSE60424$Label
#' # res <-  estimateCellTypeProportions(method = "Music",
#' #                                    bulkdata = Bulk,
#' #                                    scdata = SC,
#' #                                    label = Label,
#' #                                    SubMethod = "weighted",
#'                                      use_docker = FALSE)
#'
#'
#'
#'
#'
estimateCellTypeProportions <- function(
  method,
  bulkdata,
  signature = NULL,
  scdata = NULL,
  label = NULL,
  celltypenum = NULL,
  tissue = NULL,
  tumortype = NULL,
  reftype = NULL,
  SubMethod = NULL,
  seed = NULL,
  use_docker = FALSE
){

  method_R <- c("Abis","Adapts","Autogenes","Bayice","Bisquerna","Bseqsc","Gedit",
                "Cdseq","Cdseq_no","Distinguisher","Cibersort","Consensustme",
                "Dcq","Debcam","Demixt","Dtangle","Dtd","Dwls","Enigma",
                "Epic","Estimate","Fardeep","Mcpcounter","Mixture","Momf",
                "Music","Nitumid","Prede","Quantiseq","Scbiocpm","Scdc",
                "Singscore","Ssgsea","Ticpe","Timer","Toast_no","Toast","Xcell")

  if(any(method==method_R)){
     if(use_docker){
     estimate_output <- container_deconvolution(method,
                                                bulkdata,
                                                signature,
                                                scdata,
                                                label,
                                                celltypenum,
                                                tissue,
                                                tumortype,
                                                SubMethod,
                                                reftype,
                                                seed)




    # Output
    cat("\n")
    cat("\n")
    cat("\n")
    cat(paste0("#######################",method," Method runs successfully ######################"))
    cat("\n")
    cat("\n")
    cat("\n")

    return(estimate_output)
  }
                     else{
    # Select the right method
    env <- asNamespace("deconvBench")
    right_method <- method
    assign(right_method, get(right_method, envir = env))
    # Match arguments existing in the method function
    arguments <- dplyr::lst(bulkdata,
                            signature,
                            scdata,
                            label,
                            celltypenum,
                            reftype,
                            tissue,
                            seed,
                            tumortype,
                            SubMethod)
    arguments <- arguments[intersect(names(arguments), names(formals(right_method)))]
    # Execute estimation step by do.call and pass the arguments to the estimation function
    estimate_output <- BiocGenerics::do.call(right_method, arguments)
    # Output
    cat("\n")
    cat("\n")
    cat("\n")
    cat(paste0("#######################",method," Method runs successfully ######################"))
    cat("\n")
    cat("\n")
    cat("\n")
    return(estimate_output)
  }
  } else{
      stop("Please enter one of the estimate cell type: Abis, Adapts, Autogenes,Bayice, Bisquerna, Bseqsc, Cdseq, Cdseq_no, Distinguisher, Cibersort, Consensustme, Dcq, Debcam, Demixt, Dtangle, Dtd, Dwls, Enigma, Epic, Estimate, Fardeep, Mcpcounter,Mixture, Momf, Music, Nitumid, Prede, Quantiseq, Scbiocpm, Scdc, Singscore, Ssgsea,Ticpe, Timer, Toast_no, Toast,Xcell.")}

 }