#' @title Post preprocess a RNA-seq dataset.
#'
#' @description This function preprocess a dataset that was already pre-processed
#' in order to perform the cleaning / filtering / batch corection steps.
#' The results are saves in TSV file(s). In addition, it returns the ESET object(s).
#'
#' Note: the function does not check for the existence of folders or files.
#'
#' @param input_data_dir A string representing the folder that contains the input data.
#' @param output_data_files An array of strings representing the files that should contain the
#' preprocessed data. At least one value, maximum two if batch_correction is "BOTH".
#' @param expression_filename A String indicating where the expression matrix can be found,
#' default to 'Expdata.tsv'.
#' @param batch_correction A String indicating whether batch correction should
#' be performed. Options are "TRUE", "FALSE", "BOTH", default to "FALSE".
#' @param batch_filename A string indicating where the batch information can be found,
#' default to 'Batch.tsv'. Note: not yet suported.
#' @param clean_samples A boolean indicating whether the dataset should be cleaned by removing
#' the samples that do not have clinical data. Default to FALSE.
#' @param remove_low_counts A boolean indicating whether low counts should be removed prior
#' to the normalization, TRUE by default.
#' @param low_count_threshold A numeric representing the maximum number of counts associated to
#' transcripts considered as not lowly expressed. Default value is 5.
#' @param verbose A boolean representing whether the function should display log information. This
#' is FALSE by default.
#' @return The expression data as ESET objects. Potentially only one object (therefore unlisted).
preprocess_data_rnaseq <- function(input_data_dir, output_data_files,
                                   expression_filename = "ExpData.tsv",
                                   batch_correction    = "FALSE",
                                   batch_filename      = "Batch.tsv",
                                   clean_samples       = FALSE,
                                   remove_low_counts   = TRUE,
                                   low_count_threshold = 5,
                                   verbose             = FALSE) {

  # We read the pre-processed data (RNA-seq).
  exp_data_fn <- paste0(input_data_dir, expression_filename)
  exp_data    <- ArrayUtils::read_eset(exp_data_fn, as_eset = FALSE)
  rm(exp_data_fn)

  # The RNA-seq data consists of counts so we need to post-process
  # it to get some kind of microarray compatible data (for instance on a log2 scale).
  # We first need to filter the low expressed transcripts.
  if (remove_low_counts) {
    keep     <- rowSums(exp_data) > low_count_threshold
    exp_data <- exp_data[keep, ]
    rm(keep)
  }

  # We need to normalize the counts.
  exp_data_dge <- edgeR::DGEList(counts = exp_data)
  exp_data_dge <- edgeR::calcNormFactors(exp_data_dge)
  rm(exp_data)

  # CPM then does the normalization and the log-transformation.
  exp_data_norm <- limma::voom(exp_data_dge, normalize.method = "quantile")$E
  rm(exp_data_dge)

  # We log information.
  if (verbose == TRUE) {
    message(paste0("[", Sys.time(), "] Raw data read"))
  }

  # We remove the probes that have 0 variance accross the samples.
  probe_vars  <- apply(exp_data_norm, 1, var)
  probe_var_0 <- names(probe_vars[probe_vars == 0])
  eset <- NULL
  if (length(probe_var_0) > 0) {
    clean_probe_list <- setdiff(rownames(exp_data_norm), probe_var_0)
    eset <- Biobase::ExpressionSet(exp_data_norm[clean_probe_list, ])
    rm(clean_probe_list)
  } else {
    eset <- Biobase::ExpressionSet(exp_data_norm)
  }

  # We clean up and log information.
  rm(exp_data_norm, probe_vars, probe_var_0)
  if (verbose == TRUE) {
    message(paste0("[", Sys.time(), "] Data cleaned (step I)."))
  }

  # We correct for the batch effect if necesary.
  eset_bc <- NULL
  if (batch_correction != "FALSE") {
    eset_bc <- correct_batch_effect(eset           = eset,
                                    input_data_dir = input_data_dir,
                                    verbose        = verbose)

    # We log some information.
    if (verbose == TRUE) {
      message(paste0("[", Sys.time(), "] Batch effect corrected."))
    }

    if (batch_correction == "TRUE") {
      eset <- eset_bc
      rm(eset_bc)
    }
  } else {
    rm(eset_bc)
  }

  # If necessary, we remove the samples that do not have clinical data.
  if (clean_samples) {
    # We load the clinical data as to get the samples to keep.
    samples <- rownames(Biobase::pData(ArrayUtils::load_clinical_data(input_data_dir,
                                                                      verbose = verbose)))
    # We only keep the samples with clinical data.
    eset <- eset[, samples]
    if (batch_correction == "BOTH") {
      eset_bc <- eset_bc[, samples]
    }

    # We clean up and log information.
    rm(samples)
    if (verbose == TRUE) {
      message(paste0("[", Sys.time(), "] Data cleaned (step II)."))
    }
  }

  # We save the eset data as TSV file.
  utils::write.table(Biobase::exprs(eset),
                     file  = output_data_files[1],
                     sep   = "\t",
                     quote = FALSE)
  if (batch_correction == "BOTH") {
    utils::write.table(Biobase::exprs(eset_bc),
                       file  = output_data_files[2],
                       sep   = "\t",
                       quote = FALSE)
  }

  # We log information.
  if (verbose == TRUE) {
    message(paste0("[", Sys.time(), "] Processed data written to files."))
  }

  # We return the created ESET(s).
  if (batch_correction == "BOTH") {
    return(list(eset, eset_bc))
  } else {
    return(eset)
  }
}