inst/examples/clustering-imputation/synthetic/indep_var_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))

indep_var_analysis <- function(opts, sim){
    # Initialize lists
    indep_prof = indep_mean = eval_perf <- vector("list", length = length(opts$cluster_var))
    i <- 1
    # Iterate
    for (cl_iter in opts$cluster_var) {
        # Load synthetic data
        io <- list(data_file = paste0("high_noise_encode_data_", cl_iter, "_", sim, ".rds"),
                   data_dir = "../../local-data/melissa/synthetic/imputation/dissimilarity/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
        region_ind <- lapply(X = 1:opts$N, FUN = function(n) which(!is.na(dt$train[[n]])))
        na_ind <- lapply(X = 1:opts$N, FUN = function(n) which(is.na(dt$train[[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))))
        # Infer MLE methylation profiles for each cell and region
        if (opts$is_parallel) { W_vb_prof <- parallel::mclapply(X = 1:opts$N, FUN = function(n)
            infer_profiles_mle(X = dt$train[[n]][region_ind[[n]]], model = "bernoulli",
                               basis = opts$basis_prof, lambda = 1/10, opt_itnmax = 40)$W,
            mc.cores = opts$no_cores)
        }else{W_vb_prof <- lapply(X = 1:opts$N, FUN = function(n)
            infer_profiles_mle(X = dt$train[[n]][region_ind[[n]]], model = "bernoulli",
                               basis = opts$basis_prof, lambda = 1/10, opt_itnmax = 40)$W)
        }
        # Infer MLE methylation rate for each cell and region
        if (opts$is_parallel) { W_vb_mean <- parallel::mclapply(X = 1:opts$N, FUN = function(n)
            infer_profiles_mle(X = dt$train[[n]][region_ind[[n]]], model = "bernoulli",
                               basis = opts$basis_mean, lambda = 1/10, opt_itnmax = 40)$W,
            mc.cores = opts$no_cores)
        }else{W_vb_mean <- lapply(X = 1:opts$N, FUN = function(n)
            infer_profiles_mle(X = dt$train[[n]][region_ind[[n]]], model = "bernoulli",
                               basis = opts$basis_mean, lambda = 1/10, opt_itnmax = 40)$W)
        }
        # Store data in array objects
        W_prof <- array(data = 0, dim = c(opts$M, opts$basis_prof$M + 1, opts$N))
        W_mean <- array(data = 0, dim = c(opts$M, 1, opts$N))
        for (n in 1:opts$N) { # Iterate over each cell
            # Store optimized W to an array object (genes x basis x cells)
            W_prof[region_ind[[n]],,n] <- W_vb_prof[[n]]
            W_mean[region_ind[[n]],,n] <- W_vb_mean[[n]]
        }
        # Cases when we have empty genomic regions, sample randomly from other cell
        # methylation profiles
        for (cell in 1:opts$N) {
            if (length(na_ind[[cell]]) > 0) {
                for (m in na_ind[[cell]]) {
                    ind_cell <- sample(cell_ind[[m]], 1)
                    W_prof[m, , cell] <- W_prof[m, , ind_cell]
                    W_mean[m, , cell] <- W_mean[m, , ind_cell]
                }
            }
        }
        indep_prof[[i]] <- W_prof
        indep_mean[[i]] <- W_mean
        # Evalute model performance
        eval_prof <- eval_performance(obj = W_prof, test = dt$test, basis = opts$basis_prof)
        eval_mean <- eval_performance(obj = W_mean, test = dt$test, basis = opts$basis_mean)
        eval_perf[[i]] <- list(eval_prof = eval_prof, eval_mean = eval_mean)
        ##----------------------------------------------------------------------
        message("Computing AUC...")
        ##----------------------------------------------------------------------
        pred_prof <- prediction(eval_prof$pred_obs, eval_prof$act_obs)
        # roc_prof <- performance(pred_prof, "tpr", "fpr")
        auc_prof <- performance(pred_prof, "auc")
        auc_prof <- unlist(auc_prof@y.values)
        message(auc_prof)
        pred_mean <- prediction(eval_mean$pred_obs, eval_mean$act_obs)
        # roc_mean <- performance(pred_mean, "tpr", "fpr")
        auc_mean <- performance(pred_mean, "auc")
        auc_mean <- unlist(auc_mean@y.values)
        message(auc_mean)
        i <- i + 1 # Increase counter
    }
    obj <- list(indep_prof = indep_prof, indep_mean = indep_mean,
                eval_perf = eval_perf, opts = opts)
    return(obj)
}



##------------------------
# Load synthetic data
##------------------------
io <- list(data_file = paste0("raw/data-sims/high_noise_encode_data_0.1_1.rds"),
           out_dir = "../../local-data/melissa/synthetic/imputation/dissimilarity/")
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)
    indep_var_analysis(opts = opts, sim = sim), mc.cores = no_cores_out)
print(date())

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