Added batch correction for all non-SCAN pre-processing methods. Changed Limma...

Added batch correction for all non-SCAN pre-processing methods. Changed Limma to add age as a co-factor when possible.

DESCRIPTION

@@ -17,6 +17,7 @@ Imports:
     gcrma,
     arrayQualityMetrics,
     SCAN.UPC,
+    sva,
     massiR,
     limma,
     stringr,

NAMESPACE

@@ -4,3 +4,6 @@ importFrom("grDevices", "dev.off", "png")
 importFrom("stats", "cor", "df", "prcomp")
 importClassesFrom("AnnotationDbi")
 importMethodsFrom("AnnotationDbi", "mget")
+importFrom("stats", "model.matrix")
+importFrom("utils", "read.delim")
+importFrom("stats", "var")

R/correct_batch_effect.R

+#' @title Correct for a batch effect.
+#'
+#' @description This function runs ComBat to correct for a batch effect. It also
+#' takes into account clinical factors as to not kill the real biological signal.
+#' It returns the corrected ESET object.
+#'
+#' Note: the function does not check for the existence of folders or files.
+#'
+#' @param eset The original expressionSet object that contains the (uncorrected) data.
+#' @param input_data_dir A string representing the folder that contains the clinical
+#' and batch data.
+#' @param is_eset A boolean representing whether the input data is an ExpressionSet object
+#' (TRUE) or a simple matrix with the expression values (FALSE). This is TRUE by default.
+#' @param batch_filename A string indicating where the batch information can be found,
+#' default to 'Batch.tsv'.
+#' @param verbose A boolean representing whether the function should display log information. This
+#' is TRUE by default.
+#' @return The corrected expression data as an ESET object.
+correct_batch_effect <- function(eset,
+                                 input_data_dir,
+                                 is_eset        = TRUE,
+                                 batch_filename = "Batch.tsv",
+                                 verbose        = TRUE) {
+
+  # We read the clinical and batch data.
+  batch_data_file <- paste0(input_data_dir, batch_filename)
+  batch_data      <- utils::read.delim(file       = batch_data_file,
+                                       row.names  = 1)
+  pheno_data      <- Biobase::pData(ArrayUtils::load_clinical_data(input_data_dir,
+                                                                   verbose = FALSE))
+  remove(batch_data_file)
+
+  # If necessary, we convert the input matrix to an ExpressionSet.
+  if (!is_eset) {
+    eset <- Biobase::ExpressionSet(eset)
+  }
+
+  # We then build the mod matrix that contains the clinically relevant co-factors.
+  # By default, we consider we have only the disease status, but we then also
+  # try to add the gender and age as co-factors to guide Combat to correct for batch effect
+  # without removing the biologically relevant signal.
+  mod_data        <- stats::model.matrix(~Disease.status, data = pheno_data)
+  if (sum(is.na(pheno_data$Gender)) == 0) {
+    if (sum(is.na(pheno_data$Age))  == 0) {
+      mod_data    <- stats::model.matrix(~Disease.status + Gender + as.numeric(as.character(Age)),
+                                  data = pheno_data)
+    } else {
+      mod_data    <- stats::model.matrix(~Disease.status + Gender, data = pheno_data)
+    }
+  }
+
+  # ComBat corrects for the batch effect.
+  data_bc <- sva::ComBat(dat       = Biobase::exprs(eset),
+                         batch     = batch_data$Batch,
+                         mod       = mod_data)
+  eset_bc <- Biobase::ExpressionSet(data_bc)
+
+  # We clean up and log information.
+  remove(batch_data, pheno_data, mod_data)
+  if (verbose == TRUE) {
+    message(paste0("[", Sys.time(), "] Batch effect corrected."))
+  }
+
+  # We return the corrected ExpressionSet or the matrix (same as input).
+  if (!is_eset) {
+    return(Biobase::exprs(eset_bc))
+  } else {
+    return(eset_bc)
+  }
+}

R/preprocess_data.R

@@ -10,73 +10,73 @@
 #' 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_file A string representing the file that should contain the
-#'  preprocessed 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 platform A string representing the array platform among Affymetrix, Illumina and Agilent.
 #' Default to Affymetrix.
 #' @param method A string representing the preprocessing method to use (for Affymetrix arrays
 #' where multiple methods are supported). Default to SCAN for Affymetrix data.
 #' @param compressed A boolean representing whether the cel files are compressed. This
 #'  is FALSE by default.
-#' @param batch_correction A boolean indicating whether batch correction should
-#'  be performed, default to FALSE.
+#' @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'.
 #' @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 verbose A boolean representing whether the function should display log information. This
 #'  is TRUE by default.
-#' @return The expression data as an ESET object.
-preprocess_data <- function(input_data_dir, output_data_file,
+#' @return The expression data as ESET objects. Potentially only one object (therefore unlisted).
+preprocess_data <- function(input_data_dir, output_data_files,
                             platform         = "Affymetix",
                             method           = "SCAN",
                             compressed       = FALSE,
-                            batch_correction = FALSE,
+                            batch_correction = "FALSE",
                             batch_filename   = "Batch.tsv",
                             clean_samples    = FALSE,
                             verbose          = TRUE) {
 
   # We launch the correct function depending on the array platform and desired method.
-  eset <- NULL
+  esets <- NULL
   if (platform == "Affymetrix") {
     if (method == "SCAN") {
-      eset <- preprocess_data_affymetrix_scan(input_data_dir,
-                                              output_data_file,
-                                              compressed       = compressed,
-                                              batch_correction = batch_correction,
-                                              batch_filename   = batch_filename,
-                                              clean_samples    = clean_samples,
-                                              verbose          = verbose)
-    } else if (method == "GCRMA") {
-      eset <- preprocess_data_affymetrix_gcrma(input_data_dir,
-                                               output_data_file,
+      esets <- preprocess_data_affymetrix_scan(input_data_dir,
+                                               output_data_files,
                                                compressed       = compressed,
                                                batch_correction = batch_correction,
                                                batch_filename   = batch_filename,
                                                clean_samples    = clean_samples,
                                                verbose          = verbose)
+    } else if (method == "GCRMA") {
+      esets <- preprocess_data_affymetrix_gcrma(input_data_dir,
+                                                output_data_files,
+                                                compressed       = compressed,
+                                                batch_correction = batch_correction,
+                                                batch_filename   = batch_filename,
+                                                clean_samples    = clean_samples,
+                                                verbose          = verbose)
     }
   } else if (platform == "Agilent") {
-    eset <- preprocess_data_agilent_limma(input_data_dir,
-                                          output_data_file,
-                                          compressed       = compressed,
-                                          batch_correction = batch_correction,
-                                          batch_filename   = batch_filename,
-                                          clean_samples    = clean_samples,
-                                          verbose          = verbose)
+    esets <- preprocess_data_agilent_limma(input_data_dir,
+                                           output_data_files,
+                                           compressed       = compressed,
+                                           batch_correction = batch_correction,
+                                           batch_filename   = batch_filename,
+                                           clean_samples    = clean_samples,
+                                           verbose          = verbose)
   } else if (platform == "Illumina") {
-    eset <- preprocess_data_illumina_beadarray(input_data_dir,
-                                               output_data_file,
-                                               compressed       = compressed,
-                                               batch_correction = batch_correction,
-                                               batch_filename   = batch_filename,
-                                               clean_samples    = clean_samples,
-                                               verbose          = verbose)
+    esets <- preprocess_data_illumina_beadarray(input_data_dir,
+                                                output_data_files,
+                                                compressed       = compressed,
+                                                batch_correction = batch_correction,
+                                                batch_filename   = batch_filename,
+                                                clean_samples    = clean_samples,
+                                                verbose          = verbose)
   } else {
     message(paste0("[", Sys.time(), "] Platform ", platform,
                    " not yet supported (no preprocessing done)."))
   }
 
-  # We return the created ESET.
-  return(eset)
+  # We return the created ESET(s).
+  return(esets)
 }

R/preprocess_data_affymetrix_gcrma.R

@@ -5,29 +5,28 @@
 #'
 #' The function assumes that a folder containing the raw data exists (as cel files).
 #'
-#' Note: the function does not check for the existence of folders or files. The batch effect
-#' correction is currently not yet supported.
+#' 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_file A string representing the file that should contain the
-#'  preprocessed 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 compressed A boolean representing whether the cel files are compressed. This
-#'  is FALSE by default.
-#' @param batch_correction A boolean indicating whether batch correction should
-#'  be performed, default to FALSE. Note: not yet suported.
+#' is FALSE by default.
+#' @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.
+#' 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.
+#' the samples that do not have clinical data. Default to FALSE.
 #' @param verbose A boolean representing whether the function should display log information. This
-#'  is TRUE by default.
-#' @return The expression data as an ESET object.
-preprocess_data_affymetrix_gcrma <- function(input_data_dir, output_data_file,
-                                           compressed       = FALSE,
-                                           batch_correction = FALSE,
-                                           batch_filename   = "Batch.tsv",
-                                           clean_samples    = FALSE,
-                                           verbose          = TRUE) {
+#' is TRUE by default.
+#' @return The expression data as ESET objects. Potentially only one object (therefore unlisted).
+preprocess_data_affymetrix_gcrma <- function(input_data_dir, output_data_files,
+                                             compressed       = FALSE,
+                                             batch_correction = "FALSE",
+                                             batch_filename   = "Batch.tsv",
+                                             clean_samples    = FALSE,
+                                             verbose          = TRUE) {
 
   # We define the I/Os.
   raw_data_input_dir <- paste0(input_data_dir, "RAW/")
@@ -38,6 +37,30 @@ preprocess_data_affymetrix_gcrma <- function(input_data_dir, output_data_file,
   batch <- affy::ReadAffy(filenames = input_data_files, compress = compressed, verbose = verbose)
   eset  <- gcrma::gcrma(batch)
 
+  # We remove the probes that have 0 variance accross the samples.
+  exp_data    <- Biobase::exprs(eset)
+  probe_vars  <- apply(exp_data, 1, var)
+  probe_var_0 <- names(probe_vars[probe_vars == 0])
+  if (length(probe_var_0) > 0) {
+    clean_probe_list <- setdiff(rownames(exp_data), probe_var_0)
+    eset <- Biobase::ExpressionSet(exp_data[clean_probe_list, ])
+    remove(clean_probe_list)
+  }
+  remove(exp_data, probe_vars, probe_var_0)
+
+  # 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)
+    if (batch_correction == "TRUE") {
+      eset <- eset_bc
+      remove(eset_bc)
+    }
+  } else {
+    remove(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.
@@ -45,10 +68,19 @@ preprocess_data_affymetrix_gcrma <- function(input_data_dir, output_data_file,
                                                                       verbose = FALSE)))
     # We only keep the samples with clinical data.
     eset <- eset[, samples]
+    if (batch_correction == "BOTH") {
+      eset_bc <- eset_bc[, samples]
+    }
   }
 
   # We save the eset data as TSV file.
-  utils::write.table(Biobase::exprs(eset), file = output_data_file, sep = "\t", quote = FALSE)
+  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 clean up and log information.
   remove(input_data_files, batch)
@@ -56,6 +88,10 @@ preprocess_data_affymetrix_gcrma <- function(input_data_dir, output_data_file,
     message(paste0("[", Sys.time(), "] Expression data pre-processed with RMA."))
   }
 
-  # We return the created ESET.
-  return(eset)
+  # We return the created ESET(s).
+  if (batch_correction == "BOTH") {
+    return(list(eset, eset_bc))
+  } else {
+    return(eset)
+  }
 }

R/preprocess_data_affymetrix_scan.R

@@ -8,23 +8,22 @@
 #' 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_file A string representing the file that should contain the
-#'  preprocessed 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 compressed A boolean representing whether the cel files are compressed. This
 #'  is FALSE by default.
-#' @param batch_correction A boolean indicating whether batch correction should
-#'  be performed, default to FALSE.
+#' @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'.
 #' @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. Note: the cleaning is not yet
-#'  suported for SCAN normalization.
+#'  the samples that do not have clinical data. Default to FALSE.
 #' @param verbose A boolean representing whether the function should display log information. This
 #'  is TRUE by default.
-#' @return The expression data as an ESET object.
-preprocess_data_affymetrix_scan <- function(input_data_dir, output_data_file,
+#' @return The expression data as ESET objects. Potentially only one object (therefore unlisted).
+preprocess_data_affymetrix_scan <- function(input_data_dir, output_data_files,
                                             compressed       = FALSE,
-                                            batch_correction = FALSE,
+                                            batch_correction = "FALSE",
                                             batch_filename   = "Batch.tsv",
                                             clean_samples    = FALSE,
                                             verbose          = TRUE) {
@@ -36,16 +35,50 @@ preprocess_data_affymetrix_scan <- function(input_data_dir, output_data_file,
   # We do not run the fast analysis (by default).
   input_data_regexp <- paste0(raw_data_input_dir, "*")
   remove(raw_data_input_dir)
-  eset <- vector()
-  if (batch_correction == FALSE) {
-    eset <- SCAN.UPC::SCAN(input_data_regexp, outFilePath = output_data_file)
+  eset <- SCAN.UPC::SCAN(input_data_regexp, outFilePath = output_data_files[1])
+
+  # We remove the probes that have 0 variance accross the samples.
+  exp_data    <- Biobase::exprs(eset)
+  probe_vars  <- apply(exp_data, 1, var)
+  probe_var_0 <- names(probe_vars[probe_vars == 0])
+  if (length(probe_var_0) > 0) {
+    clean_probe_list <- setdiff(rownames(exp_data), probe_var_0)
+    eset <- Biobase::ExpressionSet(exp_data[clean_probe_list, ])
+    remove(clean_probe_list)
+  }
+  remove(exp_data, probe_vars, probe_var_0)
+
+  # 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)
+    if (batch_correction == "TRUE") {
+      eset <- eset_bc
+      remove(eset_bc)
+    }
   } else {
-    # We define the I/Os (for batch).
-    batch_data_file <- paste0(input_data_dir, batch_filename)
-    eset <- SCAN.UPC::SCAN(input_data_regexp,
-                           outFilePath   = output_data_file,
-                           batchFilePath = batch_data_file)
-    remove(batch_data_file)
+    remove(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 = FALSE)))
+    # We only keep the samples with clinical data.
+    eset <- eset[, samples]
+    if (batch_correction == "BOTH") {
+      eset_bc <- eset_bc[, samples]
+    }
+  }
+
+  # We save the eset_bc data as TSV file. ESET was already done as part of SCAN.
+  if (batch_correction == "BOTH") {
+    utils::write.table(Biobase::exprs(eset_bc),
+                       file  = output_data_files[2],
+                       sep   = "\t",
+                       quote = FALSE)
   }
 
   # We clean up and log information.
@@ -54,6 +87,10 @@ preprocess_data_affymetrix_scan <- function(input_data_dir, output_data_file,
     message(paste0("[", Sys.time(), "] Expression data pre-processed with SCAN."))
   }
 
-  # We return the created ESET.
-  return(eset)
+  # We return the created ESET(s).
+  if (batch_correction == "BOTH") {
+    return(list(eset, eset_bc))
+  } else {
+    return(eset)
+  }
 }

R/preprocess_data_agilent_limma.R

@@ -5,26 +5,25 @@
 #'
 #' The function assumes that a folder containing the raw data exists (as cel files).
 #'
-#' Note: the function does not check for the existence of folders or files. The bacth effect
-#' correction is not yet supported.
+#' 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_file A string representing the file that should contain the
-#'  preprocessed 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 compressed A boolean representing whether the cel files are compressed. This
 #'  is FALSE by default. Note: only for compatibility reasons (has no effect on analysis).
-#' @param batch_correction A boolean indicating whether batch correction should
-#'  be performed, default to FALSE. Note: not yet suported.
+#' @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.
+#'  default to 'Batch.tsv'.
 #' @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 verbose A boolean representing whether the function should display log information. This
 #'  is TRUE by default.
-#' @return The expression data as an ESET object.
-preprocess_data_agilent_limma <- function(input_data_dir, output_data_file,
+#' @return The expression data as ESET objects. Potentially only one object (therefore unlisted).
+preprocess_data_agilent_limma <- function(input_data_dir, output_data_files,
                                           compressed       = FALSE,
-                                          batch_correction = FALSE,
+                                          batch_correction = "FALSE",
                                           batch_filename   = "Batch.tsv",
                                           clean_samples    = FALSE,
                                           verbose          = TRUE) {
@@ -54,7 +53,36 @@ preprocess_data_agilent_limma <- function(input_data_dir, output_data_file,
 
   # Second, perform quantile normalization.
   batch_data_norm <- limma::normalizeBetweenArrays(batch_data_bg, method = "quantile")
+  rm(batch_data_bg)
 
+  # We remove the duplicate rows (based on the row names only).
+  probe_id_counts  <- table(row.names(batch_data))
+  unique_probe_ids <- setdiff(names(probe_id_counts), names(probe_id_counts[probe_id_counts > 1]))
+  batch_data_norm  <- batch_data_norm[unique_probe_ids, ]
+
+  # We remove the probes that have 0 variance accross the samples.
+  probe_vars  <- apply(batch_data_norm, 1, var)
+  probe_var_0 <- names(probe_vars[probe_vars == 0])
+  if (length(probe_var_0) > 0) {
+    clean_probe_list <- setdiff(rownames(batch_data_norm), probe_var_0)
+    batch_data_norm <- batch_data_norm[clean_probe_list, ]
+    remove(clean_probe_list)
+  }
+  remove(probe_vars, probe_var_0)
+
+  # We correct for the batch effect if necesary.
+  batch_data_norm_bc <- NULL
+  if (batch_correction != "FALSE") {
+    batch_data_norm_bc <- correct_batch_effect(eset           = batch_data_norm,
+                                               input_data_dir = input_data_dir,
+                                               is_eset        = FALSE)
+    if (batch_correction == "TRUE") {
+      batch_data_norm <- batch_data_norm_bc
+      remove(batch_data_norm_bc)
+    }
+  } else {
+    remove(batch_data_norm_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.
@@ -62,24 +90,33 @@ preprocess_data_agilent_limma <- function(input_data_dir, output_data_file,
                                                                       verbose = FALSE)))
     # We only keep the samples with clinical data.
     batch_data_norm <- batch_data_norm[, samples]
+    if (batch_correction == "BOTH") {
+      batch_data_norm_bc <- batch_data_norm_bc[, samples]
+    }
   }
 
-  # We remove the duplicate rows (based on the row names only).
-  probe_id_counts  <- table(row.names(batch_data))
-  unique_probe_ids <- setdiff(names(probe_id_counts), names(probe_id_counts[probe_id_counts > 1]))
-  batch_data_norm  <- batch_data_norm[unique_probe_ids, ]
-
   # We save the eset data as TSV file.
   eset <- methods::new("ExpressionSet", exprs = as.matrix(batch_data_norm))
-  rm(batch_data_bg)
-  utils::write.table(batch_data_norm, file = output_data_file, sep = "\t", quote = FALSE)
+  utils::write.table(batch_data_norm, file = output_data_files[1], sep = "\t", quote = FALSE)
   rm(batch_data_norm)
+  eset_bc <- NULL
+  if (batch_correction == "BOTH") {
+    eset_bc <- methods::new("ExpressionSet", exprs = as.matrix(batch_data_norm_bc))
+    utils::write.table(batch_data_norm_bc, file = output_data_files[2], sep = "\t", quote = FALSE)
+    rm(batch_data_norm_bc)
+  } else {
+    remove(eset_bc)
+  }
 
   # We clean up and log information.
   if (verbose == TRUE) {
     message(paste0("[", Sys.time(), "] Expression data pre-processed with LIMMA."))
   }
 
-  # We return the created ESET.
-  return(eset)
+  # We return the created ESET(s).
+  if (batch_correction == "BOTH") {
+    return(list(eset, eset_bc))
+  } else {
+    return(eset)
+  }
 }

R/preprocess_data_illumina_beadarray.R

@@ -9,22 +9,22 @@
 #' correction is not yet supported.
 #'
 #' @param input_data_dir A string representing the folder that contains the input data.
-#' @param output_data_file A string representing the file that should contain the
-#'  preprocessed 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 compressed A boolean representing whether the cel files are compressed. This
 #'  is FALSE by default. Note: only for compatibility reasons (has no effect on analysis).
-#' @param batch_correction A boolean indicating whether batch correction should
-#'  be performed, default to FALSE. Note: not yet suported.
+#' @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.
+#'  default to 'Batch.tsv'.
 #' @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 verbose A boolean representing whether the function should display log information. This
 #'  is TRUE by default.
-#' @return The expression data as an ESET object.
-preprocess_data_illumina_beadarray <- function(input_data_dir, output_data_file,
+#' @return The expression data as ESET objects. Potentially only one object (therefore unlisted).
+preprocess_data_illumina_beadarray <- function(input_data_dir, output_data_files,
                                                compressed       = FALSE,
-                                               batch_correction = FALSE,
+                                               batch_correction = "FALSE",
                                                batch_filename   = "Batch.tsv",
                                                clean_samples    = FALSE,
                                                verbose          = TRUE) {
@@ -42,9 +42,11 @@ preprocess_data_illumina_beadarray <- function(input_data_dir, output_data_file,
   # controls probes (v3) or not (v2). The decision is based on the probe_quality field.
   gse_data_filt <- NULL
   if (is.null(Biobase::fData(gse_data)$PROBEQUALITY)) {
+
     # We have a v2 dataset, simple quantile normalization.
     gse_data_norm <- beadarray::normaliseIllumina(gse_data, method = "quantile")
-    # We log the data (using an offset for small to negatove values)
+
+    # We log the data (using an offset for small to negative values)
     offset    <- 0
     min_value <- min(Biobase::exprs(gse_data_norm))
     if (min_value < 1) {
@@ -74,6 +76,30 @@ preprocess_data_illumina_beadarray <- function(input_data_dir, output_data_file,
     rm(gse_data, gse_data_norm)
   }
 
+  # We remove the probes that have 0 variance accross the samples.
+  probe_vars  <- apply(gse_data_filt, 1, var)
+  probe_var_0 <- names(probe_vars[probe_vars == 0])
+  if (length(probe_var_0) > 0) {
+    clean_probe_list <- setdiff(rownames(gse_data_filt), probe_var_0)
+    gse_data_filt <- gse_data_filt[clean_probe_list, ]
+    remove(clean_probe_list)
+  }
+  remove(probe_vars, probe_var_0)
+
+  # We correct for the batch effect if necesary.
+  gse_data_filt_bc <- NULL
+  if (batch_correction != "FALSE") {
+    gse_data_filt_bc <- correct_batch_effect(eset           = gse_data_filt,
+                                             input_data_dir = input_data_dir,
+                                             is_eset        = FALSE)
+    if (batch_correction == "TRUE") {
+      gse_data_filt <- gse_data_filt_bc
+      remove(gse_data_filt_bc)
+    }
+  } else {
+    remove(gse_data_filt_bc)
+  }