R/run_canopy.R

In AxelitoMartin/TumorEvolution:

#' Run Canopy
#' @param sna_obj An output object from the make_sna_mat() function.
#' @param cna_obj An output object from the make_cna_mat() function.
#' @param Y An output object from the make_Y_mat() function.
#' @param projectname Name of the project to save results.
#' @param K Vector of the number of clones to be used.
#' @param numchain Number of chains for the MCMC. Default is 15.
#' @param max.simrun Maximum number of simulations to perform before convergence. Default is 100000.
#' @param min.simrun Minimum number of simulations to perform before convergence. Default is 10000.
#' @param burnin Burn-in period for the MCMC. Default is 10.
#' @param thin Thining parameter for the MCMC. Default is 5.
#' @param nCores Number of cores to be used.
#' @param plot.likelihood Should likelihoods be plotted
#' @return Y
#' @export
#' @examples
#' @import
#' dplyr
#' dtplyr
#' Canopy
#' foreach
#' doParallel
#' parallel


run_canopy <- function(sna_obj, cna_obj, Y, projectname, K, numchain = 15, max.simrun = 100000, min.simrun = 10000,
                       burnin = 10, thin = 5,nCores = NULL,plot.likelihood = FALSE){

  set.seed(21071993)

  R <- sna_obj$R
  X <- sna_obj$X
  WM <- cna_obj$WM
  Wm <- cna_obj$Wm
  epsM <- cna_obj$epsM
  epsm <- cna_obj$epsm

  if(is.null(nCores))
    sampchain = canopy.sample(R = as.matrix(R), X = as.matrix(X), WM = as.matrix(WM),
                              Wm = as.matrix(Wm), epsilonM = as.matrix(epsM),
                              epsilonm = as.matrix(epsm),
                              C = NULL, Y = as.matrix(Y), K = K,
                              numchain = numchain, max.simrun = max.simrun,
                              min.simrun = min.simrun, writeskip = 200,
                              projectname = projectname, cell.line = FALSE,
                              plot.likelihood = plot.likelihood)

  if(!is.null(nCores)){

    sampchain = canopy.sample.parallel.mod(R = as.matrix(R), X = as.matrix(X), WM = as.matrix(WM),
                                           Wm = as.matrix(Wm), epsilonM = as.matrix(epsM),
                                           epsilonm = as.matrix(epsm),
                                           C = NULL, Y = as.matrix(Y), K = K,
                                           numchain = numchain, max.simrun = max.simrun,
                                           min.simrun = min.simrun, writeskip = 200,
                                           projectname = projectname, cell.line = FALSE,
                                           plot.likelihood = plot.likelihood,nCores = nCores)
    # cl <- makeCluster(nCores)
    # registerDoParallel(cl)
    #
    # sampchain <- foreach(k=K) %dopar% {
    #   sampchainOut <-
    #     Canopy::canopy.sample(R = as.matrix(R), X = as.matrix(X), WM = as.matrix(WM),
    #                   Wm = as.matrix(Wm), epsilonM = as.matrix(epsM),
    #                   epsilonm = as.matrix(epsm),
    #                   C = NULL, Y = as.matrix(Y), K = as.numeric(k),
    #                   numchain = numchain, max.simrun = max.simrun,
    #                   min.simrun = min.simrun, writeskip = 200,
    #                   projectname = projectname, cell.line = FALSE,
    #                   plot.likelihood = FALSE)
    #   return(sampchainOut)
  }

  # stopCluster(cl)

return(sampchain)

}