inst/Simulations/02.RunMethods.R

In CNRGH/pintmf: Penalized Integrative Matrix Factorization for Multiomics Data

library("parallel")
library(PintMF)
library(future.apply)
source("inst/Simulations/00.setup.R")
listBenchmark <- list.files(pathDat)
nbCPU <- 2
for(ii in 5:8){
  b <- listBenchmark[ii]
  K <- nclust[ii]
  pathDat_sim <- Arguments$getReadablePathname(sprintf("%s/%s", pathDat, b))
  pathMeth_sub <- Arguments$getReadablePathname(sprintf("%s/%s", pathMeth, b))
  print(pathMeth_sub)
  list.sim <- list.files(pathDat_sim, full.names = TRUE) %>% lapply(readRDS)
  data <- lapply(list.sim, function (ll) ll$data)
  remove_zero <- function (dat){
    lapply(dat, function(dd){
      idx <- which(colSums(dd)==0)
      if(length(idx)!=0){
        return(dd[, -idx])
      }else{
        return(dd)
      }
    })
  }
  data_filter <- data %>% lapply(remove_zero)
  data_t_filter <- lapply(data_filter, function(dd){
    dd[[3]] <- log2(dd[[3]]/(1-dd[[3]]))
    dd
  })
   print("my_meth")
   my_meth_results <- lapply(data_t_filter, SolveInt, p=K, max.it=5, flavor_mod = "glmnet", init_flavor = "snf")
   saveRDS(my_meth_results, file=file.path(pathMeth_sub, sprintf("my_meth_res.rds")))
}


## COCA
library(coca)
for(ii in 1:8){
  b <- listBenchmark[ii]
  K <- nclust[ii]
  pathDat_sim <- Arguments$getReadablePathname(sprintf("%s/%s", pathDat, b))
  pathMeth_sub <- Arguments$getReadablePathname(sprintf("%s/%s", pathMeth, b))
  print(pathMeth_sub)
  list.sim <- list.files(pathDat_sim, full.names = TRUE) %>% lapply(readRDS)
  data <- lapply(list.sim, function (ll) ll$data)
  remove_zero <- function (dat){
    lapply(dat, function(dd){
      idx <- which(colSums(dd)==0)
      if(length(idx)!=0){
        return(dd[, -idx])
      }else{
        return(dd)
      }
    })
  }
  data_filter <- data %>% lapply(remove_zero)
  data_t_filter <- lapply(data_filter, function(dd){
    dd[[3]] <- log2(dd[[3]]/(1-dd[[3]]))
    dd
  })
  print("coca")
  outputBuildMOC <- lapply(data_t_filter, buildMOC, M = 3, K = K, distances = "cor")
  # Extract matrix of clusters
  moc <- lapply(outputBuildMOC, function (out) out$moc)

  # Do Cluster-Of-Clusters Analysis
  outputCOCA <- lapply(moc, function (mm) coca(mm, K = K))

  # Extract cluster labels
  clusterLabels <- lapply(outputCOCA, function (outcoca) outcoca$clusterLabels)

  saveRDS(clusterLabels, file=file.path(pathMeth_sub, sprintf("coca_res.rds")))
}