inst/Simulations_b1to4/R_scripts/01.runMethods.R

In CNRGH/crimmix: Multi-omics Integration

pathDat <- R.utils::Arguments$getWritablePath("Data_sim_20181012")
pathMeth <- R.utils::Arguments$getWritablePath("Data_Results_20181012")
S <- 50
nclust=4
n_byClust=c(10,20,5,25)
nbCPU=15

grid.noise <- c(0.1, 0.2, 0.5, 1)
grid.param <- list(noiseD1=c(0.2, 0.5, 0.1, 0.2),
                   noiseD2=c(0.1, 0.1, 0.5, 0.2)/10,
                   noiseD3=c(0.1, 0.1, 0.1, 0.5)*3)
props <- c(0.005, 0.01, 0.02)

library("parallel")
for(ii in 1:4){
  pathDat_sim <- R.utils::Arguments$getWritablePath(sprintf("%s/Benchmark%s", pathDat, ii))
  pathMeth_sub <- R.utils::Arguments$getWritablePath(sprintf("%s/Benchmark%s", pathMeth, ii))

  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)

  print("SNF")
  SNFresults <- mclapply(data, IntMultiOmics, method="SNF", K=4, mc.cores = nbCPU)
  saveRDS(SNFresults, file=file.path(pathMeth_sub, sprintf("SNF_res.rds")))

  print("kernel")
  Kernelresults <- mclapply(data_filter, IntMultiOmics, method="MixKernel", K=4, mc.cores = nbCPU)
  saveRDS(Kernelresults, file=file.path(pathMeth_sub, sprintf("Kernel_res.rds")))


  print("MCIA")
  MCIAresults <- mclapply(data_filter, IntMultiOmics, method="MCIA", K=4, mc.cores = nbCPU)
  saveRDS(MCIAresults, file=file.path(pathMeth_sub, sprintf("MCIA_res.rds")))


  print("Mocluster")
  Moaresults <- mclapply(data, IntMultiOmics, method="Mocluster", K=4, ncomp=4, k=c(0.05,0.4, 0.1), mc.cores = nbCPU)
  saveRDS(Moaresults, file=file.path(pathMeth_sub, sprintf("Mocluster_res.rds")))

  print("RGCCA")
  RGCCAresults <- mclapply(data, IntMultiOmics, method="RGCCA", K=4, mc.cores = nbCPU)
  saveRDS(RGCCAresults, file=file.path(pathMeth_sub, sprintf("RGCCA_res.rds")))

  print("NMF")
  NMFresults <- mclapply(data_filter, IntMultiOmics, method="intNMF", K=4, mc.cores = nbCPU)
  saveRDS(NMFresults, file=file.path(pathMeth_sub, sprintf("NMF_res.rds")))

  print("SGCCA")
  SGCCAresults <- mclapply(data_filter, IntMultiOmics, method="SGCCA", K=4, c1= c(0.3, 0.3,0.4),
                           ncomp=rep(3, 3), mc.cores = nbCPU)
  saveRDS(SGCCAresults, file=file.path(pathMeth_sub, sprintf("SGCCA_res.rds")))

  print("icluster")
  iCluster_results <- mclapply(data_filter, IntMultiOmics, method="iCluster", K=3, lambda= c(0.03, 0.03,0.03),
                               type=c("gaussian", "binomial", "gaussian"), mc.cores = nbCPU)
  saveRDS(iCluster_results, file=file.path(pathMeth_sub, sprintf("iCluster_res.rds")))

  print("MOFA")
  Mofaresults <- lapply(1:length(data_filter), function (dd) {
    IntMultiOmics(data_filter[[dd]], method="MOFA", K=4)
  }) %>% save(file=file.path(pathMeth_sub, sprintf("MOFA_res.rds")))

}