inst/examples/clustering-imputation/synthetic/rf_indep_cell_eval_rep.R

In andreaskapou/Melissa: Bayesian clustering and imputationa of single cell methylomes

# ------------------------------------------
# Set working directory and load libraries
# ------------------------------------------
if (interactive()) {cur.dir <- dirname(parent.frame(2)$ofile); setwd(cur.dir)}
R.utils::sourceDirectory("../../lib", modifiedOnly = FALSE)
suppressPackageStartupMessages(library(BPRMeth))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(ROCR))

rf_indep_var_analysis <- function(opts, sim){
  # Initialize lists
  eval_perf <- vector("list", length = length(opts$N))
  i <- 1
  # Iterate
  for (cl_iter in opts$N) {
    # Load synthetic data
    io <- list(data_file = paste0("encode_data_", cl_iter, "_", sim, ".rds"),
               data_dir = "../../local-data/melissa/synthetic/imputation/cells/raw/data-sims/")
    obj <- readRDS(paste0(io$data_dir, io$data_file))
    # Partition to training and test sets
    dt <- partition_dataset(X = obj$synth_data$X, region_train_prcg = opts$region_train_prcg,
                            cpg_train_prcg = opts$cpg_train_prcg, is_synth = TRUE)

    # List of genes with no coverage for each cell
    train_region_ind <- lapply(X = 1:cl_iter, FUN = function(n) which(!is.na(dt$train[[n]])))
    test_region_ind <- lapply(X = 1:cl_iter, FUN = function(n) which(!is.na(dt$test[[n]])))
    # List of cells with no coverage for each genomic region
    cell_ind <- lapply(X = 1:opts$M, FUN = function(m) which(!is.na(lapply(dt$train, "[[", m))))
    # Use RF for prediction

    act_obs = pred_obs <- vector("numeric")
    for (n in 1:cl_iter) { # Iterate over the cells
      for (m in test_region_ind[[n]]) { # Iterate over genomic regions
        if (m %in% train_region_ind[[n]]) {
          y <- as.factor(dt$train[[n]][[m]][,2])
          if (length(levels(y)) == 1) {
            if (as.numeric(levels(y)) == 1) {
              dt$train[[n]][[m]] <- rbind(dt$train[[n]][[m]], c(0.1, 0))
            }else{
              dt$train[[n]][[m]] <- rbind(dt$train[[n]][[m]], c(0.1, 1))
            }
          }
          model <- randomForest::randomForest(x = dt$train[[n]][[m]][,1, drop = FALSE],
                                              y = as.factor(dt$train[[n]][[m]][,2]),
                                              ntree = 50, nodesize = 2)
          pred_obs <- c(pred_obs, predict(object = model, newdata = dt$test[[n]][[m]][,1, drop = FALSE],
                                          type = "prob")[,2])
          act_obs <- c(act_obs, dt$test[[n]][[m]][,2])
        } else{
          # Randomly sample a different cell that has coverage and predict from its profile
          ind_cell <- sample(cell_ind[[m]], 1)
          y <- as.factor(dt$train[[ind_cell]][[m]][,2])
          if (length(levels(y)) == 1) {
            if (as.numeric(levels(y)) == 1) {
              dt$train[[ind_cell]][[m]] <- rbind(dt$train[[ind_cell]][[m]], c(0.1, 0))
            }else{
              dt$train[[ind_cell]][[m]] <- rbind(dt$train[[ind_cell]][[m]], c(0.1, 1))
            }
          }
          model <- randomForest::randomForest(x = dt$train[[ind_cell]][[m]][,1, drop = FALSE],
                                              y = as.factor(dt$train[[ind_cell]][[m]][,2]),
                                              ntree = 50, nodesize = 2)
          pred_obs <- c(pred_obs, predict(object = model, newdata = dt$test[[n]][[m]][,1, drop = FALSE],
                                          type = "prob")[,2])
          act_obs <- c(act_obs, dt$test[[n]][[m]][,2])
        }
      }
    }

    # Store evaluated performance
    eval_perf[[i]] <- list(act_obs = act_obs, pred_obs = pred_obs)
    ##----------------------------------------------------------------------
    message("Computing AUC...")
    ##----------------------------------------------------------------------
    pred_rf <- prediction(pred_obs, act_obs)
    # roc_prof <- performance(pred_prof, "tpr", "fpr")
    auc_rf <- performance(pred_rf, "auc")
    auc_rf <- unlist(auc_rf@y.values)
    message(auc_rf)
    i <- i + 1 # Increase counter
  }
  obj <- list(eval_perf = eval_perf, opts = opts)
  return(obj)
}



##------------------------
# Load synthetic data
##------------------------
io <- list(data_file = paste0("raw/data-sims/encode_data_25_1.rds"),
           out_dir = "../../local-data/melissa/synthetic/imputation/cells/")
obj <- readRDS(paste0(io$out_dir, io$data_file))
opts                   <- obj$opts # Get options
opts$data_train_prcg   <- 0.1      # % of data to keep fully for training
opts$region_train_prcg <- 1        # % of regions kept for training
opts$cpg_train_prcg    <- 0.4      # % of CpGs kept for training in each region
opts$is_parallel       <- TRUE     # Use parallelized version
opts$no_cores          <- 3        # Number of cores
rm(obj)

# Parallel analysis
no_cores_out <- BPRMeth:::.parallel_cores(no_cores = opts$total_sims,
                                          is_parallel = TRUE)
print(date())
obj <- parallel::mclapply(X = 1:opts$total_sims, FUN = function(sim)
  rf_indep_var_analysis(opts = opts, sim = sim), mc.cores = no_cores_out)
print(date())

##----------------------------------------------------------------------
message("Storing results...")
##----------------------------------------------------------------------
saveRDS(obj, file = paste0(io$out_dir, "encode_rf_indep_K", opts$K,
                           "_rbf", opts$basis_prof$M,
                           "_dataTrain", opts$data_train_prcg,
                           "_regionTrain", opts$region_train_prcg,
                           "_cpgTrain", opts$cpg_train_prcg, ".rds") )