diff --git a/DESCRIPTION b/DESCRIPTION
index 5659be3..509025f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,34 +1,66 @@
-Package: SC3
 Type: Package
+Package: SC3
 Title: Single-Cell Consensus Clustering
 Version: 1.15.1
+Authors@R: 
+    c(person(given = "Vladimir",
+             family = "Kiselev",
+             role = c("cre", "aut"),
+             email = "vladimir.yu.kiselev@gmail.com"),
+      person(given = "Andrew",
+             family = "Yiu",
+             role = "ctb"),
+      person(given = "Tallulah",
+             family = "Andrews",
+             role = "ctb"),
+      person(given = "Martin",
+             family = "Hemberg",
+             role = "aut"))
 Author: Vladimir Kiselev
 Maintainer: Vladimir Kiselev <vladimir.yu.kiselev@gmail.com>
-Authors@R: c(person("Vladimir", "Kiselev",
-             email = "vladimir.yu.kiselev@gmail.com",
-             role=c("cre", "aut")),
-             person("Andrew", "Yiu",
-             role=c("ctb")),
-             person("Tallulah", "Andrews",
-             role=c("ctb")),
-             person("Martin", "Hemberg",
-             role=c("aut")))
-Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data.
+Description: A tool for unsupervised clustering and analysis of single
+    cell RNA-Seq data.
 License: GPL-3
-Imports: graphics, stats, utils, methods, e1071, parallel, foreach,
-          doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8),
-          ROCR, robustbase, rrcov, cluster, WriteXLS,
-          Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment,
-          BiocGenerics, S4Vectors
-Depends: R(>= 3.3)
-LinkingTo: Rcpp, RcppArmadillo
-LazyData: TRUE
-RoxygenNote: 6.0.1
-Suggests: knitr, rmarkdown, mclust, scater
-VignetteBuilder: knitr
-biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, 
-            SupportVectorMachine, RNASeq, Visualization, Transcriptomics, 
-            DataRepresentation, GUI, DifferentialExpression, Transcription
-NeedsCompilation: no
 URL: https://github.com/hemberg-lab/SC3
 BugReports: https://support.bioconductor.org/t/sc3/
+Depends: 
+    R (>= 3.3)
+Imports: 
+    BiocGenerics,
+    BiocParallel,
+    cluster,
+    e1071,
+    ggplot2,
+    graphics,
+    methods,
+    pheatmap (>= 1.0.8),
+    Rcpp (>= 0.11.1),
+    robustbase,
+    ROCR,
+    rrcov,
+    S4Vectors,
+    shiny,
+    SingleCellExperiment,
+    stats,
+    SummarizedExperiment,
+    utils,
+    WriteXLS
+Suggests: 
+    BiocStyle,
+    knitr,
+    mclust,
+    rmarkdown,
+    scater
+LinkingTo: 
+    Rcpp,
+    RcppArmadillo
+VignetteBuilder: 
+    knitr
+biocViews: ImmunoOncology, SingleCell, Software, Classification,
+    Clustering, DimensionReduction, SupportVectorMachine, RNASeq,
+    Visualization, Transcriptomics, DataRepresentation, GUI,
+    DifferentialExpression, Transcription
+Encoding: UTF-8
+LazyData: TRUE
+NeedsCompilation: no
+RoxygenNote: 7.1.1
diff --git a/NAMESPACE b/NAMESPACE
index 167add9..7df85a5 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -39,11 +39,7 @@ importFrom(SummarizedExperiment,assayNames)
 importFrom(SummarizedExperiment,colData)
 importFrom(SummarizedExperiment,rowData)
 importFrom(WriteXLS,WriteXLS)
-importFrom(doParallel,registerDoParallel)
-importFrom(doRNG,"%dorng%")
 importFrom(e1071,svm)
-importFrom(foreach,"%dopar%")
-importFrom(foreach,foreach)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,geom_bar)
 importFrom(ggplot2,ggplot)
@@ -53,9 +49,6 @@ importFrom(ggplot2,ylim)
 importFrom(graphics,plot)
 importFrom(methods,as)
 importFrom(methods,new)
-importFrom(parallel,detectCores)
-importFrom(parallel,makeCluster)
-importFrom(parallel,stopCluster)
 importFrom(pheatmap,pheatmap)
 importFrom(robustbase,covMcd)
 importFrom(rrcov,PcaHubert)
diff --git a/R/AllGenerics.R b/R/AllGenerics.R
index 60cb602..b3ee6ea 100644
--- a/R/AllGenerics.R
+++ b/R/AllGenerics.R
@@ -1,49 +1,48 @@
 #' @export
-setGeneric("sc3", signature = "object", function(object, ks = NULL, 
-        gene_filter = TRUE, pct_dropout_min = 10, pct_dropout_max = 90, 
-        d_region_min = 0.04, d_region_max = 0.07, svm_num_cells = NULL, 
-        svm_train_inds = NULL, svm_max = 5000, n_cores = NULL, kmeans_nstart = NULL, 
-        kmeans_iter_max = 1e+09, k_estimator = FALSE, biology = FALSE, rand_seed = 1) {
+setGeneric("sc3", signature = "object", function(object, ks = NULL,
+        gene_filter = TRUE, pct_dropout_min = 10, pct_dropout_max = 90,
+        d_region_min = 0.04, d_region_max = 0.07, svm_num_cells = NULL,
+        svm_train_inds = NULL, svm_max = 5000, kmeans_nstart = NULL,
+        kmeans_iter_max = 1e+09, k_estimator = FALSE, biology = FALSE,
+        BPPARAM = BiocParallel::bpparam()) {
     standardGeneric("sc3")
 })
 
 #' @export
-setGeneric("sc3_estimate_k", signature = "object", function(object) {
+setGeneric("sc3_estimate_k", signature = "object", function(object, BPPARAM = BiocParallel::bpparam()) {
     standardGeneric("sc3_estimate_k")
 })
 
 #' @export
-setGeneric("sc3_prepare", function(object, gene_filter = TRUE, 
-        pct_dropout_min = 10, pct_dropout_max = 90, d_region_min = 0.04, 
-        d_region_max = 0.07, svm_num_cells = NULL, svm_train_inds = NULL, 
-        svm_max = 5000, n_cores = NULL, kmeans_nstart = NULL, 
-        kmeans_iter_max = 1e+09, rand_seed = 1) {
+setGeneric("sc3_prepare", function(object, gene_filter = TRUE,
+        pct_dropout_min = 10, pct_dropout_max = 90, d_region_min = 0.04,
+        d_region_max = 0.07, svm_num_cells = NULL, svm_train_inds = NULL,
+        svm_max = 5000, kmeans_nstart = NULL, kmeans_iter_max = 1e+09) {
     standardGeneric("sc3_prepare")
 })
 
 #' @export
-setGeneric("sc3_calc_dists", signature = "object", function(object) {
+setGeneric("sc3_calc_dists", signature = "object", function(object, BPPARAM = BiocParallel::bpparam()) {
     standardGeneric("sc3_calc_dists")
 })
 
 #' @export
-setGeneric("sc3_calc_transfs", signature = "object", function(object) {
+setGeneric("sc3_calc_transfs", signature = "object", function(object, BPPARAM = BiocParallel::bpparam()) {
     standardGeneric("sc3_calc_transfs")
 })
 
 #' @export
-setGeneric("sc3_kmeans", signature = "object", function(object, ks = NULL) {
+setGeneric("sc3_kmeans", signature = "object", function(object, ks = NULL, BPPARAM = BiocParallel::bpparam()) {
     standardGeneric("sc3_kmeans")
 })
 
 #' @export
-setGeneric("sc3_calc_consens", signature = "object", function(object) {
+setGeneric("sc3_calc_consens", signature = "object", function(object, BPPARAM = BiocParallel::bpparam()) {
     standardGeneric("sc3_calc_consens")
 })
 
 #' @export
-setGeneric("sc3_calc_biology", signature = "object", function(object, ks = NULL, 
-                                                              regime = NULL) {
+setGeneric("sc3_calc_biology", signature = "object", function(object, ks = NULL, regime = NULL, BPPARAM = BiocParallel::bpparam()) {
     standardGeneric("sc3_calc_biology")
 })
 
@@ -58,7 +57,7 @@ setGeneric("sc3_run_svm", signature = "object", function(object, ks = NULL) {
 })
 
 #' @export
-setGeneric("sc3_plot_consensus", signature = "object", function(object, k, 
+setGeneric("sc3_plot_consensus", signature = "object", function(object, k,
                                                                 show_pdata = NULL) {
     standardGeneric("sc3_plot_consensus")
 })
@@ -74,13 +73,13 @@ setGeneric("sc3_plot_expression", signature = "object", function(object, k, show
 })
 
 #' @export
-setGeneric("sc3_plot_de_genes", signature = "object", function(object, 
+setGeneric("sc3_plot_de_genes", signature = "object", function(object,
                                         k, p.val = 0.01, show_pdata = NULL) {
     standardGeneric("sc3_plot_de_genes")
 })
 
 #' @export
-setGeneric("sc3_plot_markers", signature = "object", function(object, k, auroc = 0.85, 
+setGeneric("sc3_plot_markers", signature = "object", function(object, k, auroc = 0.85,
                                         p.val = 0.01, show_pdata = NULL) {
     standardGeneric("sc3_plot_markers")
 })
@@ -91,7 +90,7 @@ setGeneric("sc3_plot_cluster_stability", signature = "object", function(object,
 })
 
 #' @export
-setGeneric("sc3_export_results_xls", signature = "object", function(object, 
+setGeneric("sc3_export_results_xls", signature = "object", function(object,
                                                 filename = "sc3_results.xls") {
     standardGeneric("sc3_export_results_xls")
 })
diff --git a/R/CoreMethods.R b/R/CoreMethods.R
index 46b12f4..1243654 100644
--- a/R/CoreMethods.R
+++ b/R/CoreMethods.R
@@ -1,49 +1,46 @@
 #' Run all steps of \code{SC3} in one go
-#' 
+#'
 #' This function is a wrapper that executes all steps of \code{SC3} analysis in one go.
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class.
 #' @param ks a range of the number of clusters \code{k} used for \code{SC3} clustering.
 #' Can also be a single integer.
-#' @param gene_filter a boolen variable which defines whether to perform gene 
+#' @param gene_filter a boolen variable which defines whether to perform gene
 #' filtering before SC3 clustering.
-#' @param pct_dropout_min if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than 
+#' @param pct_dropout_min if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than
 #' \code{pct_dropout_min} are filtered out before clustering.
-#' @param pct_dropout_max if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than 
+#' @param pct_dropout_max if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than
 #' \code{pct_dropout_max} are filtered out before clustering.
-#' @param d_region_min defines the minimum number of eigenvectors used for 
+#' @param d_region_min defines the minimum number of eigenvectors used for
 #' kmeans clustering as a fraction of the total number of cells. Default is \code{0.04}.
 #' See \code{SC3} paper for more details.
-#' @param d_region_max defines the maximum number of eigenvectors used for 
+#' @param d_region_max defines the maximum number of eigenvectors used for
 #' kmeans clustering as a fraction of the total number of cells. Default is \code{0.07}.
 #' See \code{SC3} paper for more details.
-#' @param svm_num_cells number of randomly selected training cells to be used 
+#' @param svm_num_cells number of randomly selected training cells to be used
 #' for SVM prediction. The default is \code{NULL}.
-#' @param svm_train_inds a numeric vector defining indeces of training cells 
+#' @param svm_train_inds a numeric vector defining indeces of training cells
 #' that should be used for SVM training. The default is \code{NULL}.
 #' @param svm_max define the maximum number of cells below which SVM is not run.
-#' @param n_cores defines the number of cores to be used on the user's machine. If not set, `SC3` will use all but one cores of your machine.
-#' @param kmeans_nstart nstart parameter passed to \code{\link[stats]{kmeans}} function. Can be set manually. By default it is 
+#' @param kmeans_nstart nstart parameter passed to \code{\link[stats]{kmeans}} function. Can be set manually. By default it is
 #' \code{1000} for up to \code{2000} cells and \code{50} for more than \code{2000} cells.
-#' @param kmeans_iter_max iter.max parameter passed to \code{\link[stats]{kmeans}} 
+#' @param kmeans_iter_max iter.max parameter passed to \code{\link[stats]{kmeans}}
 #' function.
 #' @param k_estimator boolean parameter, defines whether to estimate an optimal number of clusters \code{k}. If user has already defined the ks parameter the estimation does not affect the user's paramater.
-#' @param biology boolean parameter, defines whether to compute differentially expressed genes, marker 
+#' @param biology boolean parameter, defines whether to compute differentially expressed genes, marker
 #' genes and cell outliers.
-#' @param rand_seed sets the seed of the random number generator. \code{SC3} is a stochastic
-#' method, so setting the \code{rand_seed} to a fixed values can be used for reproducibility
-#' purposes.
-#' 
+#' @param BPPARAM a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+#' including a seed for random number generator.
+#'
 #' @name sc3
 #' @aliases sc3
-#' 
+#'
 #' @return an object of \code{SingleCellExperiment} class
-sc3.SingleCellExperiment <- function(object, ks, gene_filter, pct_dropout_min, pct_dropout_max, d_region_min, 
-                       d_region_max, svm_num_cells, svm_train_inds, svm_max, n_cores, kmeans_nstart, kmeans_iter_max, 
-                       k_estimator, biology, rand_seed) {
-    object <- sc3_prepare(object, gene_filter, pct_dropout_min, pct_dropout_max, 
-        d_region_min, d_region_max, svm_num_cells, svm_train_inds, svm_max, n_cores, kmeans_nstart, 
-        kmeans_iter_max, rand_seed)
+sc3.SingleCellExperiment <- function(object, ks, gene_filter, pct_dropout_min, pct_dropout_max, d_region_min,
+                       d_region_max, svm_num_cells, svm_train_inds, svm_max, kmeans_nstart, kmeans_iter_max,
+                       k_estimator, biology, BPPARAM) {
+    object <- sc3_prepare(object, gene_filter, pct_dropout_min, pct_dropout_max,
+        d_region_min, d_region_max, svm_num_cells, svm_train_inds, svm_max, kmeans_nstart, kmeans_iter_max)
     if (k_estimator) {
         object <- sc3_estimate_k(object)
         # Do not override cluster if user has set a k
@@ -52,12 +49,17 @@ sc3.SingleCellExperiment <- function(object, ks, gene_filter, pct_dropout_min, p
             ks <- metadata(object)$sc3$k_estimation
         }
     }
-    object <- sc3_calc_dists(object)
-    object <- sc3_calc_transfs(object)
-    object <- sc3_kmeans(object, ks)
-    object <- sc3_calc_consens(object)
+
+    if (!BiocParallel::bpisup(BPPARAM)) {
+        BiocParallel::bpstart(BPPARAM)
+    }
+
+    object <- sc3_calc_dists(object, BPPARAM = BPPARAM)
+    object <- sc3_calc_transfs(object, BPPARAM = BPPARAM)
+    object <- sc3_kmeans(object, ks = ks, BPPARAM = BPPARAM)
+    object <- sc3_calc_consens(object, BPPARAM = BPPARAM)
     if (biology) {
-        object <- sc3_calc_biology(object, ks)
+        object <- sc3_calc_biology(object, ks, BPPARAM = BPPARAM)
     }
     return(object)
 }
@@ -67,7 +69,7 @@ sc3.SingleCellExperiment <- function(object, ks, gene_filter, pct_dropout_min, p
 setMethod("sc3", signature(object = "SingleCellExperiment"), sc3.SingleCellExperiment)
 
 #' Prepare the \code{SingleCellExperiment} object for \code{SC3} clustering.
-#' 
+#'
 #' This function prepares an object of \code{SingleCellExperiment} class for \code{SC3} clustering. It
 #' creates and populates the following items of the \code{sc3} slot of the \code{metadata(object)}:
 #' \itemize{
@@ -75,55 +77,48 @@ setMethod("sc3", signature(object = "SingleCellExperiment"), sc3.SingleCellExper
 #'   \item \code{kmeans_nstart} - the same as the \code{kmeans_nstart} argument.
 #'   \item \code{n_dim} - contains numbers of the number of eigenvectors to be used
 #'   in \code{\link[stats]{kmeans}} clustering.
-#'   \item \code{rand_seed} - the same as the \code{rand_seed} argument.
-#'   \item \code{svm_train_inds} - if SVM is used this item contains indexes of the 
+#'   \item \code{svm_train_inds} - if SVM is used this item contains indexes of the
 #'   training cells to be used for SC3 clustering and further SVM prediction.
 #'   \item \code{svm_study_inds} - if SVM is used this item contains indexes of the
 #'    cells to be predicted by SVM.
-#'   \item \code{n_cores} - the same as the \code{n_cores} argument.
 #' }
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class.
-#' @param gene_filter a boolen variable which defines whether to perform gene 
+#' @param gene_filter a boolen variable which defines whether to perform gene
 #' filtering before SC3 clustering.
-#' @param pct_dropout_min if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than 
+#' @param pct_dropout_min if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than
 #' \code{pct_dropout_min} are filtered out before clustering.
-#' @param pct_dropout_max if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than 
+#' @param pct_dropout_max if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than
 #' \code{pct_dropout_max} are filtered out before clustering.
-#' @param d_region_min defines the minimum number of eigenvectors used for 
+#' @param d_region_min defines the minimum number of eigenvectors used for
 #' kmeans clustering as a fraction of the total number of cells. Default is \code{0.04}.
 #' See \code{SC3} paper for more details.
-#' @param d_region_max defines the maximum number of eigenvectors used for 
+#' @param d_region_max defines the maximum number of eigenvectors used for
 #' kmeans clustering as a fraction of the total number of cells. Default is \code{0.07}.
 #' See \code{SC3} paper for more details.
-#' @param svm_num_cells number of randomly selected training cells to be used 
+#' @param svm_num_cells number of randomly selected training cells to be used
 #' for SVM prediction. The default is \code{NULL}.
-#' @param svm_train_inds a numeric vector defining indeces of training cells 
+#' @param svm_train_inds a numeric vector defining indeces of training cells
 #' that should be used for SVM training. The default is \code{NULL}.
 #' @param svm_max define the maximum number of cells below which SVM is not run.
-#' @param n_cores defines the number of cores to be used on the user's machine. If not set, `SC3` will use all but one cores of your machine.
-#' @param kmeans_nstart nstart parameter passed to \code{\link[stats]{kmeans}} function. Default is 
+#' @param kmeans_nstart nstart parameter passed to \code{\link[stats]{kmeans}} function. Default is
 #' \code{1000} for up to \code{2000} cells and \code{50} for more than \code{2000} cells.
-#' @param kmeans_iter_max iter.max parameter passed to \code{\link[stats]{kmeans}} 
+#' @param kmeans_iter_max iter.max parameter passed to \code{\link[stats]{kmeans}}
 #' function. Default is \code{1e+09}.
-#' @param rand_seed sets the seed of the random number generator. \code{SC3} is a stochastic
-#' method, so setting the \code{rand_seed} to a fixed values can be used for reproducibility
-#' purposes.
-#' 
+#'
 #' @name sc3_prepare
 #' @aliases sc3_prepare sc3_prepare,SingleCellExperiment-method
-#' 
+#'
 #' @return an object of \code{SingleCellExperiment} class
-#' 
-#' @importFrom parallel detectCores
+#'
 #' @importFrom SummarizedExperiment colData colData<- rowData rowData<- assayNames
 #' @importFrom S4Vectors metadata metadata<-
 #' @importFrom utils capture.output
 #' @importFrom methods new
 #' @importFrom BiocGenerics counts
-sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_min, pct_dropout_max, 
-                               d_region_min, d_region_max, svm_num_cells, svm_train_inds, svm_max, n_cores, kmeans_nstart, 
-                               kmeans_iter_max, rand_seed) {
+sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_min, pct_dropout_max,
+                               d_region_min, d_region_max, svm_num_cells, svm_train_inds, svm_max, kmeans_nstart, kmeans_iter_max) {
+
     if (is.null(rowData(object)$feature_symbol)) {
         stop("There is no `feature_symbol` column in the `rowData` slot of your dataset! Please write your gene/transcript names to `rowData(object)$feature_symbol`!")
         return(object)
@@ -136,14 +131,14 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
         stop("There is no `logcounts` slot in your input SingleCellExperiment object! SC3 operates on `logcounts` slot, which is supposed to contain both normalised and log-transformed expression values! Please write these values the slot by setting `logcounts(object) <- log_norm_counts`!")
         return(object)
     }
-    
+
     message("Setting SC3 parameters...")
-    
+
     # clean up after the previous SC3 run sc3 slot
     metadata(object)$sc3 <- list()
     colData(object) <- colData(object)[, !grepl("sc3_", colnames(colData(object))), drop = FALSE]
     rowData(object) <- rowData(object)[, !grepl("sc3_", colnames(rowData(object))), drop = FALSE]
-    
+
     # gene filter
     f_data <- rowData(object)
     f_data$sc3_gene_filter <- TRUE
@@ -156,7 +151,7 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
         }
     }
     rowData(object) <- as(f_data, "DataFrame")
-    
+
     metadata(object)$sc3$kmeans_iter_max <- kmeans_iter_max
     if (is.null(kmeans_nstart)) {
         if (ncol(object) > 2000) {
@@ -168,14 +163,14 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
     } else {
         metadata(object)$sc3$kmeans_nstart <- kmeans_nstart
     }
-    
+
     # define number of cells and region of dimensions
     n_dim <- floor(d_region_min * ncol(object)):ceiling(d_region_max * ncol(object))
     # for large datasets restrict the region of dimensions to 15
     if (length(n_dim) > 15) {
         n_dim <- sample(n_dim, 15)
     }
-    
+
     # prepare for SVM
     if (!is.null(svm_num_cells) | !is.null(svm_train_inds) | ncol(object) > svm_max) {
         # handle all possible errors
@@ -183,7 +178,7 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
             if (!is.null(svm_train_inds)) {
                 return(message("You have set both svm_num_cells and svm_train_inds parameters for SVM training. Please set only one of them and rerun sc3_prepare()."))
             }
-            if (svm_num_cells >= ncol(object) - 1) 
+            if (svm_num_cells >= ncol(object) - 1)
                 return(message("Number of cells used for SVM training is larger (or equal) than the total number of cells in your dataset. Please make svm_num_cells parameter smaller and rerun sc3_prepare()."))
             if (svm_num_cells < 10) {
                 return(message("Number of cells used for SVM training is less than 10. Please make sure the number of clusters k is smaller than 10 or increase the number of training cells."))
@@ -199,10 +194,10 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
         }
         # run SVM
         tmp <- prepare_for_svm(ncol(object), svm_num_cells, svm_train_inds, svm_max)
-        
+
         metadata(object)$sc3$svm_train_inds <- tmp$svm_train_inds
         metadata(object)$sc3$svm_study_inds <- tmp$svm_study_inds
-        
+
         # update kmeans_nstart after defining SVM training indeces
         if (is.null(kmeans_nstart)) {
             if (length(tmp$svm_train_inds) <= 2000) {
@@ -211,7 +206,7 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
         } else {
             metadata(object)$sc3$kmeans_nstart <- kmeans_nstart
         }
-        
+
         # update the region of dimensions
         n_dim <- floor(d_region_min * length(tmp$svm_train_inds)):ceiling(d_region_max * length(tmp$svm_train_inds))
         # for large datasets restrict the region of dimensions to 15
@@ -219,25 +214,9 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
             n_dim <- sample(n_dim, 15)
         }
     }
-    
+
     metadata(object)$sc3$n_dim <- n_dim
-    
-    metadata(object)$sc3$rand_seed <- rand_seed
-    
-    # register computing cluster (N-1 CPUs) on a local machine
-    if (is.null(n_cores)) {
-        n_cores <- parallel::detectCores()
-        if (is.null(n_cores)) {
-            return("Cannot define a number of available CPU cores that can be used by SC3. Try to set the n_cores parameter in the sc3() function call.")
-        }
-        # leave one core for the user
-        if (n_cores > 1) {
-            n_cores <- n_cores - 1
-        }
-    }
-    
-    metadata(object)$sc3$n_cores <- n_cores
-    
+
     return(object)
 }
 
@@ -246,14 +225,14 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
 setMethod("sc3_prepare", signature(object = "SingleCellExperiment"), sc3_prepare.SingleCellExperiment)
 
 #' Estimate the optimal number of cluster \code{k} for a scRNA-Seq expression matrix
-#' 
+#'
 #' Uses Tracy-Widom theory on random matrices to estimate the optimal number of
 #' clusters \code{k}. It creates and populates the \code{k_estimation} item of the
 #' \code{sc3} slot of the \code{metadata(object)}.
-#' 
+#'
 #' @name sc3_estimate_k
 #' @aliases sc3_estimate_k sc3_estimate_k,SingleCellExperiment-method
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
 #' @return an estimated value of k
 sc3_estimate_k.SingleCellExperiment <- function(object) {
@@ -269,61 +248,46 @@ sc3_estimate_k.SingleCellExperiment <- function(object) {
 setMethod("sc3_estimate_k", signature(object = "SingleCellExperiment"), sc3_estimate_k.SingleCellExperiment)
 
 #' Calculate distances between the cells.
-#' 
+#'
 #' This function calculates distances between the cells. It
 #' creates and populates the following items of the \code{sc3} slot of the \code{metadata(object)}:
 #' \itemize{
 #'   \item \code{distances} - contains a list of distance matrices corresponding to
 #'   Euclidean, Pearson and Spearman distances.
 #' }
-#' 
+#'
 #' @name sc3_calc_dists
 #' @aliases sc3_calc_dists, sc3_calc_dists,SingleCellExperiment-method
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
-#' 
+#' @param BPPARAM a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+#' including a seed for random number generator.
+#'
 #' @return an object of \code{SingleCellExperiment} class
-#' 
-#' @importFrom doRNG %dorng%
-#' @importFrom foreach foreach %dopar%
-#' @importFrom parallel makeCluster stopCluster
-#' @importFrom doParallel registerDoParallel
-sc3_calc_dists.SingleCellExperiment <- function(object) {
+sc3_calc_dists.SingleCellExperiment <- function(object, BPPARAM) {
     dataset <- get_processed_dataset(object)
-    
+
     # check whether in the SVM regime
     if (!is.null(metadata(object)$sc3$svm_train_inds)) {
         dataset <- dataset[, metadata(object)$sc3$svm_train_inds]
     }
-    
+
     # NULLing the variables to avoid notes in R CMD CHECK
     i <- NULL
-    
+
     distances <- c("euclidean", "pearson", "spearman")
-    
+
     message("Calculating distances between the cells...")
-    
-    if (metadata(object)$sc3$n_cores > length(distances)) {
-        n_cores <- length(distances)
-    } else {
-        n_cores <- metadata(object)$sc3$n_cores
-    }
-    
-    cl <- parallel::makeCluster(n_cores, outfile = "")
-    doParallel::registerDoParallel(cl, cores = n_cores)
-    
+
     # calculate distances in parallel
-    dists <- foreach::foreach(i = distances) %dorng% {
+    dists <- BiocParallel::bplapply(distances, BPPARAM = BPPARAM, FUN = function(i, dataset) {
         try({
             calculate_distance(dataset, i)
         })
-    }
-    
-    # stop local cluster
-    parallel::stopCluster(cl)
-    
+    }, dataset = dataset)
+
     names(dists) <- distances
-    
+
     metadata(object)$sc3$distances <- dists
     return(object)
 }
@@ -333,69 +297,54 @@ sc3_calc_dists.SingleCellExperiment <- function(object) {
 setMethod("sc3_calc_dists", signature(object = "SingleCellExperiment"), sc3_calc_dists.SingleCellExperiment)
 
 #' Calculate transformations of the distance matrices.
-#' 
-#' This function transforms all \code{distances} items of the \code{sc3} slot of 
-#' the \code{metadata(object)} using either principal component analysis (PCA) 
+#'
+#' This function transforms all \code{distances} items of the \code{sc3} slot of
+#' the \code{metadata(object)} using either principal component analysis (PCA)
 #' or by calculating the eigenvectors of the associated graph Laplacian.
-#' The columns of the resulting matrices are then sorted in descending order 
-#' by their corresponding eigenvalues. The first \code{d} columns 
-#' (where \code{d = max(metadata(object)$sc3$n_dim)}) of each transformation are then 
+#' The columns of the resulting matrices are then sorted in descending order
+#' by their corresponding eigenvalues. The first \code{d} columns
+#' (where \code{d = max(metadata(object)$sc3$n_dim)}) of each transformation are then
 #' written to the \code{transformations} item of the \code{sc3} slot.
 #' Additionally, this function also removes the previously calculated \code{distances} from
 #' the \code{sc3} slot, as they are not needed for further analysis.
-#' 
+#'
 #' @name sc3_calc_transfs
 #' @aliases sc3_calc_transfs, sc3_calc_transfs,SingleCellExperiment-method
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
-#' 
+#' @param BPPARAM a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+#' including a seed for random number generator.
+#'
 #' @return an object of \code{SingleCellExperiment} class
-#' 
-#' @importFrom doRNG %dorng%
-#' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
-#' @importFrom doParallel registerDoParallel
-sc3_calc_transfs.SingleCellExperiment <- function(object) {
+sc3_calc_transfs.SingleCellExperiment <- function(object, BPPARAM) {
     dists <- metadata(object)$sc3$distances
     if (is.null(dists)) {
         stop(paste0("Please run sc3_calc_dists() first!"))
         return(object)
     }
-    
+
     # NULLing the variables to avoid notes in R CMD CHECK
     i <- NULL
-    
+
     distances <- names(dists)
     transformations <- c("pca", "laplacian")
-    
+
     n_dim <- metadata(object)$sc3$n_dim
-    
+
     hash.table <- expand.grid(dists = distances, transfs = transformations, stringsAsFactors = FALSE)
-    
+
     message("Performing transformations and calculating eigenvectors...")
-    
-    if (metadata(object)$sc3$n_cores > nrow(hash.table)) {
-        n_cores <- nrow(hash.table)
-    } else {
-        n_cores <- metadata(object)$sc3$n_cores
-    }
-    
-    cl <- parallel::makeCluster(n_cores, outfile = "")
-    doParallel::registerDoParallel(cl, cores = n_cores)
-    
+
     # calculate the 6 distinct transformations in parallel
-    transfs <- foreach::foreach(i = 1:nrow(hash.table)) %dorng% {
+    transfs <- BiocParallel::bplapply(1:nrow(hash.table), BPPARAM = BPPARAM, FUN = function(i, hash.table, dists, n_dim) {
         try({
             tmp <- transformation(get(hash.table[i, 1], dists), hash.table[i, 2])
             tmp[, 1:max(n_dim)]
         })
-    }
-    
-    # stop local cluster
-    parallel::stopCluster(cl)
-    
+    }, hash.table = hash.table, dists = dists, n_dim = n_dim)
+
     names(transfs) <- paste(hash.table[, 1], hash.table[, 2], sep = "_")
-    
+
     metadata(object)$sc3$transformations <- transfs
     # remove distances after calculating transformations
     metadata(object)$sc3$distances <- NULL
@@ -412,77 +361,67 @@ sc3_calc_transfs.SingleCellExperiment <- function(object) {
 setMethod("sc3_calc_transfs", signature(object = "SingleCellExperiment"), sc3_calc_transfs.SingleCellExperiment)
 
 #' \code{kmeans} clustering of cells.
-#' 
-#' This function performs \code{\link[stats]{kmeans}} clustering of the matrices 
+#'
+#' This function performs \code{\link[stats]{kmeans}} clustering of the matrices
 #' contained in the \code{transformations} item of the \code{sc3} slot of the \code{metadata(object)}. It then
 #' creates and populates the following items of the \code{sc3} slot:
 #' \itemize{
 #'   \item \code{kmeans} - contains a list of kmeans clusterings.
 #' }
-#' 
+#'
 #' @name sc3_kmeans
 #' @aliases sc3_kmeans, sc3_kmeans,SingleCellExperiment-method
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
 #' @param ks a continuous range of integers - the number of clusters \code{k} to be used for SC3 clustering.
 #' Can also be a single integer.
-#' 
+#'
 #' @return an object of \code{SingleCellExperiment} class
-#' 
-#' @importFrom doRNG %dorng%
-#' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
-#' @importFrom doParallel registerDoParallel
+#' @param BPPARAM a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+#' including a seed for random number generator.
+#'
 #' @importFrom utils setTxtProgressBar txtProgressBar
 #' @importFrom stats kmeans
-sc3_kmeans.SingleCellExperiment <- function(object, ks) {
+sc3_kmeans.SingleCellExperiment <- function(object, ks, BPPARAM) {
     if (is.null(ks)) {
         stop(paste0("Please provide a range of the number of clusters `ks` to be used by SC3!"))
         return(object)
     }
-    
+
     transfs <- metadata(object)$sc3$transformations
     if (is.null(transfs)) {
         stop(paste0("Please run sc3_calc_transfs() first!"))
         return(object)
     }
-    
+
     # NULLing the variables to avoid notes in R CMD CHECK
     i <- NULL
-    
+
     n_dim <- metadata(object)$sc3$n_dim
-    
+
     hash.table <- expand.grid(transf = names(transfs), ks = ks, n_dim = n_dim, stringsAsFactors = FALSE)
-    
+
     message("Performing k-means clustering...")
-    
-    n_cores <- metadata(object)$sc3$n_cores
-    
+
     kmeans_iter_max <- metadata(object)$sc3$kmeans_iter_max
     kmeans_nstart <- metadata(object)$sc3$kmeans_nstart
-    
-    cl <- parallel::makeCluster(n_cores, outfile = "")
-    doParallel::registerDoParallel(cl, cores = n_cores)
-    
+
     pb <- utils::txtProgressBar(min = 1, max = nrow(hash.table), style = 3)
-    
+
     # calculate the 6 distinct transformations in parallel
-    labs <- foreach::foreach(i = 1:nrow(hash.table)) %dorng% {
+    labs <- BiocParallel::bplapply(1:nrow(hash.table), BPPARAM = BPPARAM, FUN = function(i, hash.table, transfs, n_dim, kmeans_iter_max, kmeans_nstart, pb) {
         try({
             utils::setTxtProgressBar(pb, i)
             transf <- get(hash.table$transf[i], transfs)
-            stats::kmeans(transf[, 1:hash.table$n_dim[i]], hash.table$ks[i], iter.max = kmeans_iter_max, 
-                nstart = kmeans_nstart)$cluster
+            stats::kmeans(transf[, 1:hash.table$n_dim[i]], hash.table$ks[i], iter.max = kmeans_iter_max,
+                          nstart = kmeans_nstart)$cluster
         })
-    }
-    
+    }, hash.table = hash.table, transfs = transfs, n_dim = n_dim, kmeans_iter_max = kmeans_iter_max, kmeans_nstart = kmeans_nstart, pb = pb)
+
     close(pb)
-    
-    # stop local cluster
-    parallel::stopCluster(cl)
-    
+
     names(labs) <- paste(hash.table$transf, hash.table$ks, hash.table$n_dim, sep = "_")
-    
+
     metadata(object)$sc3$kmeans <- labs
     return(object)
 }
@@ -492,60 +431,49 @@ sc3_kmeans.SingleCellExperiment <- function(object, ks) {
 setMethod("sc3_kmeans", signature(object = "SingleCellExperiment"), sc3_kmeans.SingleCellExperiment)
 
 #' Calculate consensus matrix.
-#' 
+#'
 #' This function calculates consensus matrices based on the clustering solutions
 #' contained in the \code{kmeans} item of the \code{sc3} slot of the \code{metadata(object)}. It then
-#' creates and populates the \code{consensus} item of the \code{sc3} slot with 
+#' creates and populates the \code{consensus} item of the \code{sc3} slot with
 #' consensus matrices, their hierarchical clusterings in \code{hclust} objects,
-#' and Silhouette indeces of the clusters. It also removes the previously 
+#' and Silhouette indeces of the clusters. It also removes the previously
 #' calculated \code{kmeans} clusterings from
 #' the \code{sc3} slot, as they are not needed for further analysis.
-#' 
+#'
 #' Additionally, it also adds new columns to the \code{colData} slot of the
 #' input \code{object}. The column names correspond to the consensus cell labels
-#' and have the following format: \code{sc3_k_clusters}, where \code{k} is the 
+#' and have the following format: \code{sc3_k_clusters}, where \code{k} is the
 #' number of clusters.
-#' 
+#'
 #' @name sc3_calc_consens
 #' @aliases sc3_calc_consens, sc3_calc_consens,SingleCellExperiment-method
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
-#' 
+#' @param BPPARAM a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+#' including a seed for random number generator.
+#'
 #' @return an object of \code{SingleCellExperiment} class
-#' 
-#' @importFrom doRNG %dorng%
-#' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
-#' @importFrom doParallel registerDoParallel
+#'
 #' @import cluster
 #' @importFrom stats hclust dist as.dist
-#' 
+#'
 #' @useDynLib SC3
 #' @import Rcpp
-sc3_calc_consens.SingleCellExperiment <- function(object) {
+sc3_calc_consens.SingleCellExperiment <- function(object, BPPARAM) {
     k.means <- metadata(object)$sc3$kmeans
     if (is.null(k.means)) {
         stop(paste0("Please run sc3_kmeans() first!"))
         return(object)
     }
-    
+
     # NULLing the variables to avoid notes in R CMD CHECK
     i <- NULL
-    
+
     ks <- as.numeric(unique(unlist(lapply(strsplit(names(k.means), "_"), "[[", 3))))
-    
-    if (metadata(object)$sc3$n_cores > length(ks)) {
-        n_cores <- length(ks)
-    } else {
-        n_cores <- metadata(object)$sc3$n_cores
-    }
-    
+
     message("Calculating consensus matrix...")
-    
-    cl <- parallel::makeCluster(n_cores, outfile = "")
-    doParallel::registerDoParallel(cl, cores = n_cores)
-    
-    cons <- foreach::foreach(i = ks) %dorng% {
+
+    cons <- BiocParallel::bplapply(ks, BPPARAM = BPPARAM, FUN = function(i, k.means) {
         try({
             d <- k.means[grep(paste0("_", i, "_"), names(k.means))]
             d <- matrix(unlist(d), nrow = length(d[[1]]))
@@ -556,16 +484,13 @@ sc3_calc_consens.SingleCellExperiment <- function(object) {
             diss <- stats::as.dist(as.matrix(stats::as.dist(tmp)))
             hc <- stats::hclust(diss)
             clusts <- reindex_clusters(hc, i)
-            
+
             silh <- cluster::silhouette(clusts, diss)
-            
+
             list(consensus = dat, hc = hc, silhouette = silh)
         })
-    }
-    
-    # stop local cluster
-    parallel::stopCluster(cl)
-    
+    }, k.means = k.means)
+
     names(cons) <- ks
     if(is.null(metadata(object)$sc3$consensus)) {
         metadata(object)$sc3$consensus <- list()
@@ -573,10 +498,10 @@ sc3_calc_consens.SingleCellExperiment <- function(object) {
     for (n in names(cons)) {
         metadata(object)$sc3$consensus[[n]] <- cons[[n]]
     }
-    
+
     # remove kmeans results after calculating consensus
     metadata(object)$sc3$kmeans <- NULL
-    
+
     p_data <- colData(object)
     for (k in ks) {
         hc <- metadata(object)$sc3$consensus[[as.character(k)]]$hc
@@ -590,7 +515,7 @@ sc3_calc_consens.SingleCellExperiment <- function(object) {
         p_data[, paste0("sc3_", k, "_clusters")] <- factor(clusts, levels = sort(unique(clusts)))
     }
     colData(object) <- as(p_data, "DataFrame")
-    
+
     return(object)
 }
 
@@ -600,54 +525,52 @@ setMethod("sc3_calc_consens", signature(object = "SingleCellExperiment"), sc3_ca
 
 
 #' Calculate DE genes, marker genes and cell outliers.
-#' 
-#' This function calculates differentially expressed (DE) genes, marker genes 
+#'
+#' This function calculates differentially expressed (DE) genes, marker genes
 #' and cell outliers based on the consensus \code{SC3} clusterings.
-#' 
-#' DE genes are calculated using \code{\link{get_de_genes}}. Results of the DE 
-#' analysis are saved as new columns in the 
-#' \code{featureData} slot of the input \code{object}. The column names correspond 
-#' to the adjusted \code{p-value}s of the genes and have the following format: 
+#'
+#' DE genes are calculated using \code{\link{get_de_genes}}. Results of the DE
+#' analysis are saved as new columns in the
+#' \code{featureData} slot of the input \code{object}. The column names correspond
+#' to the adjusted \code{p-value}s of the genes and have the following format:
 #' \code{sc3_k_de_padj}, where \code{k} is the number of clusters.
-#' 
-#' Marker genes are calculated using \code{\link{get_marker_genes}}. 
-#' Results of the marker gene analysis are saved as three new 
-#' columns (for each \code{k}) to the 
-#' \code{featureData} slot of the input \code{object}. The column names correspond 
-#' to the \code{SC3} cluster labels, to the adjusted \code{p-value}s of the genes 
+#'
+#' Marker genes are calculated using \code{\link{get_marker_genes}}.
+#' Results of the marker gene analysis are saved as three new
+#' columns (for each \code{k}) to the
+#' \code{featureData} slot of the input \code{object}. The column names correspond
+#' to the \code{SC3} cluster labels, to the adjusted \code{p-value}s of the genes
 #' and to the area under the ROC curve
-#' and have the following format: \code{sc3_k_markers_clusts}, 
-#' \code{sc3_k_markers_padj} and \code{sc3_k_markers_auroc}, where \code{k} is 
+#' and have the following format: \code{sc3_k_markers_clusts},
+#' \code{sc3_k_markers_padj} and \code{sc3_k_markers_auroc}, where \code{k} is
 #' the number of clusters.
-#' 
-#' Outlier cells are calculated using \code{\link{get_outl_cells}}. Results of the 
-#' cell outlier analysis are saved as new columns in the 
-#' \code{phenoData} slot of the input \code{object}. The column names correspond 
-#' to the \code{log2(outlier_score)} and have the following format: 
+#'
+#' Outlier cells are calculated using \code{\link{get_outl_cells}}. Results of the
+#' cell outlier analysis are saved as new columns in the
+#' \code{phenoData} slot of the input \code{object}. The column names correspond
+#' to the \code{log2(outlier_score)} and have the following format:
 #' \code{sc3_k_log2_outlier_score}, where \code{k} is the number of clusters.
-#' 
+#'
 #' Additionally, \code{biology} item is added to the \code{sc3} slot and is set to
 #' \code{TRUE} indicating that the biological analysis of the dataset has been
 #' performed.
-#' 
+#'
 #' @name sc3_calc_biology
 #' @aliases sc3_calc_biology, sc3_calc_biology,SingleCellExperiment-method
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
 #' @param ks a continuous range of integers - the number of clusters \code{k} to be used for SC3 clustering.
 #' Can also be a single integer.
 #' @param regime defines what biological analysis to perform. "marker" for
 #' marker genes, "de" for differentiall expressed genes and "outl" for outlier
 #' cells
-#' 
+#' @param BPPARAM a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+#' including a seed for random number generator.
+#'
 #' @return an object of \code{SingleCellExperiment} class
-#' 
-#' @importFrom doRNG %dorng%
-#' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
-#' @importFrom doParallel registerDoParallel
+#'
 #' @importFrom methods as
-sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
+sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime, BPPARAM) {
     if (is.null(metadata(object)$sc3$consensus)) {
         stop(paste0("Please run sc3_consensus() first!"))
         return(object)
@@ -667,11 +590,11 @@ sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
         stop(paste0("Regime value must be either 'marker', 'de' or 'outl', or any combination of these three!"))
         return(object)
     }
-    
+
     message("Calculating biology...")
-    
+
     hash.table <- expand.grid(ks = ks, regime = regime, stringsAsFactors = FALSE)
-    
+
     dataset <- get_processed_dataset(object)
     p_data <- colData(object)
     clusts <- as.data.frame(p_data[, grep("sc3_.*_clusters", colnames(p_data))])
@@ -682,30 +605,18 @@ sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
         dataset <- dataset[, metadata(object)$sc3$svm_train_inds]
         clusts <- clusts[metadata(object)$sc3$svm_train_inds, ]
     }
-    
+
     # NULLing the variables to avoid notes in R CMD CHECK
     i <- NULL
-    
-    if (metadata(object)$sc3$n_cores > nrow(hash.table)) {
-        n_cores <- nrow(hash.table)
-    } else {
-        n_cores <- metadata(object)$sc3$n_cores
-    }
-    
-    cl <- parallel::makeCluster(n_cores, outfile = "")
-    doParallel::registerDoParallel(cl, cores = n_cores)
-    
-    biol <- foreach::foreach(i = 1:nrow(hash.table)) %dorng% {
+
+    biol <- BiocParallel::bplapply(1:nrow(hash.table), BPPARAM = BPPARAM, FUN = function(i, dataset, hash.table, clusts) {
         try({
             get_biolgy(dataset, clusts[, paste0("sc3_", hash.table[i, 1], "_clusters")], hash.table[i, 2])
         })
-    }
-    
-    # stop local cluster
-    parallel::stopCluster(cl)
-    
+    }, dataset = dataset, hash.table = hash.table, clusts = clusts)
+
     names(biol) <- paste(hash.table$ks, hash.table$regime, sep = "_")
-    
+
     f_data <- as.data.frame(rowData(object))
     p_data <- as.data.frame(colData(object))
     for (b in names(biol)) {
@@ -721,11 +632,11 @@ sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
             f_data[, paste0("sc3_", k, "_markers_clusts")] <- NA
             f_data[, paste0("sc3_", k, "_markers_padj")] <- NA
             f_data[, paste0("sc3_", k, "_markers_auroc")] <- NA
-            f_data[, paste0("sc3_", k, "_markers_clusts")][which(f_data$sc3_gene_filter)] <- biol[[b]][, 
+            f_data[, paste0("sc3_", k, "_markers_clusts")][which(f_data$sc3_gene_filter)] <- biol[[b]][,
                 2]
-            f_data[, paste0("sc3_", k, "_markers_padj")][which(f_data$sc3_gene_filter)] <- biol[[b]][, 
+            f_data[, paste0("sc3_", k, "_markers_padj")][which(f_data$sc3_gene_filter)] <- biol[[b]][,
                 3]
-            f_data[, paste0("sc3_", k, "_markers_auroc")][which(f_data$sc3_gene_filter)] <- biol[[b]][, 
+            f_data[, paste0("sc3_", k, "_markers_auroc")][which(f_data$sc3_gene_filter)] <- biol[[b]][,
                 1]
         }
         # save cell outliers
@@ -742,9 +653,9 @@ sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
     }
     rowData(object) <- as(f_data, "DataFrame")
     colData(object) <- as(p_data, "DataFrame")
-    
+
     metadata(object)$sc3$biology <- TRUE
-    
+
     return(object)
 }
 
@@ -753,24 +664,24 @@ sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
 setMethod("sc3_calc_biology", signature(object = "SingleCellExperiment"), sc3_calc_biology.SingleCellExperiment)
 
 #' Run the hybrid \code{SVM} approach.
-#' 
+#'
 #' This method parallelize \code{SVM} prediction for each \code{k} (the number
-#' of clusters). Namely, for each \code{k}, \code{\link{support_vector_machines}} 
+#' of clusters). Namely, for each \code{k}, \code{\link{support_vector_machines}}
 #' function is utilized to predict the labels of study cells. Training cells are
 #' selected using \code{svm_train_inds} item of the \code{sc3} slot of the
 #' \code{metadata(object)}.
-#' 
-#' Results are written to the \code{sc3_k_clusters} columns to the 
-#' \code{colData} slot of the input \code{object}, where \code{k} is the 
+#'
+#' Results are written to the \code{sc3_k_clusters} columns to the
+#' \code{colData} slot of the input \code{object}, where \code{k} is the
 #' number of clusters.
-#' 
+#'
 #' @name sc3_run_svm
 #' @aliases sc3_run_svm, sc3_run_svm,SingleCellExperiment-method
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
 #' @param ks a continuous range of integers - the number of clusters \code{k} to be used for SC3 clustering.
 #' Can also be a single integer.
-#' 
+#'
 #' @return an object of \code{SingleCellExperiment} class
 sc3_run_svm.SingleCellExperiment <- function(object, ks) {
     if (is.null(metadata(object)$sc3$svm_train_inds)) {
@@ -781,23 +692,23 @@ sc3_run_svm.SingleCellExperiment <- function(object, ks) {
         stop(paste0("Please provide a range of the number of clusters `ks` to be used by SC3!"))
         return(object)
     }
-    
+
     dataset <- get_processed_dataset(object)
     p_data <- colData(object)
     svm_train_inds <- metadata(object)$sc3$svm_train_inds
     svm_study_inds <- metadata(object)$sc3$svm_study_inds
-    
+
     for (k in ks) {
         clusts <- p_data[, paste0("sc3_", k, "_clusters")]
         clusts <- clusts[svm_train_inds]
-        
+
         train.dataset <- dataset[, svm_train_inds]
         colnames(train.dataset) <- clusts
-        
+
         study.labs <- support_vector_machines(train.dataset, dataset[, svm_study_inds], "linear")
         svm.labs <- c(clusts, study.labs)
         ord <- order(c(svm_train_inds, svm_study_inds))
-        
+
         p_data[, paste0("sc3_", k, "_clusters")] <- svm.labs[ord]
     }
     colData(object) <- as(p_data, "DataFrame")
@@ -809,26 +720,26 @@ sc3_run_svm.SingleCellExperiment <- function(object, ks) {
 setMethod("sc3_run_svm", signature(object = "SingleCellExperiment"), sc3_run_svm.SingleCellExperiment)
 
 #' Write \code{SC3} results to Excel file
-#' 
+#'
 #' This function writes all \code{SC3} results to an excel file.
-#' 
+#'
 #' @param object an object of \code{SingleCellExperiment} class
 #' @param filename name of the excel file, to which the results will be written
-#' 
+#'
 #' @name sc3_export_results_xls
 #' @aliases sc3_export_results_xls
-#' 
+#'
 #' @importFrom WriteXLS WriteXLS
 sc3_export_results_xls.SingleCellExperiment <- function(object, filename) {
     if (is.null(metadata(object)$sc3$consensus)) {
         stop(paste0("Please run sc3_consensus() first!"))
     }
-    
+
     p_data <- colData(object)
     f_data <- rowData(object)
-    
+
     res <- list()
-    
+
     if(length(grep("sc3_", colnames(p_data))) != 0) {
         cells <- as.data.frame(p_data[, grep("sc3_", colnames(p_data))])
         colnames(cells) <- colnames(p_data)[grep("sc3_", colnames(p_data))]
@@ -845,9 +756,9 @@ sc3_export_results_xls.SingleCellExperiment <- function(object, filename) {
     } else {
         warning("There is no gene data provided by SC3!")
     }
-    
+
     if(length(res) != 0) {
-        WriteXLS(res, ExcelFileName = filename, SheetNames = names(res), 
+        WriteXLS(res, ExcelFileName = filename, SheetNames = names(res),
             row.names = TRUE, AdjWidth = TRUE)
     } else {
         warning("There are no SC3 results in your data object, the Excel file will not be produced. Please run SC3 first!")
diff --git a/man/ann.Rd b/man/ann.Rd
index 74a7ba0..3cf9f87 100644
--- a/man/ann.Rd
+++ b/man/ann.Rd
@@ -4,7 +4,9 @@
 \name{ann}
 \alias{ann}
 \title{Cell type annotations for data extracted from a publication by Yan et al.}
-\format{An object of class \code{data.frame} with 90 rows and 1 columns.}
+\format{
+An object of class \code{data.frame} with 90 rows and 1 columns.
+}
 \source{
 \url{http://dx.doi.org/10.1038/nsmb.2660}
 
diff --git a/man/sc3.Rd b/man/sc3.Rd
index ca4c06f..3700de5 100644
--- a/man/sc3.Rd
+++ b/man/sc3.Rd
@@ -1,24 +1,46 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3}
 \alias{sc3}
 \alias{sc3.SingleCellExperiment}
 \alias{sc3,SingleCellExperiment-method}
-\alias{sc3}
 \title{Run all steps of \code{SC3} in one go}
 \usage{
-sc3.SingleCellExperiment(object, ks, gene_filter, pct_dropout_min,
-  pct_dropout_max, d_region_min, d_region_max, svm_num_cells, svm_train_inds,
-  svm_max, n_cores, kmeans_nstart, kmeans_iter_max, k_estimator, biology,
-  rand_seed)
-
-\S4method{sc3}{SingleCellExperiment}(object, ks = NULL, gene_filter = TRUE,
-  pct_dropout_min = 10, pct_dropout_max = 90, d_region_min = 0.04,
-  d_region_max = 0.07, svm_num_cells = NULL, svm_train_inds = NULL,
-  svm_max = 5000, n_cores = NULL, kmeans_nstart = NULL,
-  kmeans_iter_max = 1e+09, k_estimator = FALSE, biology = FALSE,
-  rand_seed = 1)
+sc3.SingleCellExperiment(
+  object,
+  ks,
+  gene_filter,
+  pct_dropout_min,
+  pct_dropout_max,
+  d_region_min,
+  d_region_max,
+  svm_num_cells,
+  svm_train_inds,
+  svm_max,
+  kmeans_nstart,
+  kmeans_iter_max,
+  k_estimator,
+  biology,
+  BPPARAM
+)
+
+\S4method{sc3}{SingleCellExperiment}(
+  object,
+  ks = NULL,
+  gene_filter = TRUE,
+  pct_dropout_min = 10,
+  pct_dropout_max = 90,
+  d_region_min = 0.04,
+  d_region_max = 0.07,
+  svm_num_cells = NULL,
+  svm_train_inds = NULL,
+  svm_max = 5000,
+  kmeans_nstart = NULL,
+  kmeans_iter_max = 1e+09,
+  k_estimator = FALSE,
+  biology = FALSE,
+  BPPARAM = BiocParallel::bpparam()
+)
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class.}
@@ -26,47 +48,44 @@ sc3.SingleCellExperiment(object, ks, gene_filter, pct_dropout_min,
 \item{ks}{a range of the number of clusters \code{k} used for \code{SC3} clustering.
 Can also be a single integer.}
 
-\item{gene_filter}{a boolen variable which defines whether to perform gene 
+\item{gene_filter}{a boolen variable which defines whether to perform gene
 filtering before SC3 clustering.}
 
-\item{pct_dropout_min}{if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than 
+\item{pct_dropout_min}{if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than
 \code{pct_dropout_min} are filtered out before clustering.}
 
-\item{pct_dropout_max}{if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than 
+\item{pct_dropout_max}{if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than
 \code{pct_dropout_max} are filtered out before clustering.}
 
-\item{d_region_min}{defines the minimum number of eigenvectors used for 
+\item{d_region_min}{defines the minimum number of eigenvectors used for
 kmeans clustering as a fraction of the total number of cells. Default is \code{0.04}.
 See \code{SC3} paper for more details.}
 
-\item{d_region_max}{defines the maximum number of eigenvectors used for 
+\item{d_region_max}{defines the maximum number of eigenvectors used for
 kmeans clustering as a fraction of the total number of cells. Default is \code{0.07}.
 See \code{SC3} paper for more details.}
 
-\item{svm_num_cells}{number of randomly selected training cells to be used 
+\item{svm_num_cells}{number of randomly selected training cells to be used
 for SVM prediction. The default is \code{NULL}.}
 
-\item{svm_train_inds}{a numeric vector defining indeces of training cells 
+\item{svm_train_inds}{a numeric vector defining indeces of training cells
 that should be used for SVM training. The default is \code{NULL}.}
 
 \item{svm_max}{define the maximum number of cells below which SVM is not run.}
 
-\item{n_cores}{defines the number of cores to be used on the user's machine. If not set, `SC3` will use all but one cores of your machine.}
-
-\item{kmeans_nstart}{nstart parameter passed to \code{\link[stats]{kmeans}} function. Can be set manually. By default it is 
+\item{kmeans_nstart}{nstart parameter passed to \code{\link[stats]{kmeans}} function. Can be set manually. By default it is
 \code{1000} for up to \code{2000} cells and \code{50} for more than \code{2000} cells.}
 
-\item{kmeans_iter_max}{iter.max parameter passed to \code{\link[stats]{kmeans}} 
+\item{kmeans_iter_max}{iter.max parameter passed to \code{\link[stats]{kmeans}}
 function.}
 
 \item{k_estimator}{boolean parameter, defines whether to estimate an optimal number of clusters \code{k}. If user has already defined the ks parameter the estimation does not affect the user's paramater.}
 
-\item{biology}{boolean parameter, defines whether to compute differentially expressed genes, marker 
+\item{biology}{boolean parameter, defines whether to compute differentially expressed genes, marker
 genes and cell outliers.}
 
-\item{rand_seed}{sets the seed of the random number generator. \code{SC3} is a stochastic
-method, so setting the \code{rand_seed} to a fixed values can be used for reproducibility
-purposes.}
+\item{BPPARAM}{a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+including a seed for random number generator.}
 }
 \value{
 an object of \code{SingleCellExperiment} class
diff --git a/man/sc3_calc_biology.Rd b/man/sc3_calc_biology.Rd
index ee02197..7a887f8 100644
--- a/man/sc3_calc_biology.Rd
+++ b/man/sc3_calc_biology.Rd
@@ -1,19 +1,20 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_calc_biology}
 \alias{sc3_calc_biology}
 \alias{sc3_calc_biology.SingleCellExperiment}
 \alias{sc3_calc_biology,}
 \alias{sc3_calc_biology,SingleCellExperiment-method}
-\alias{sc3_calc_biology,SingleCellExperiment-method}
-\alias{sc3_calc_biology}
 \title{Calculate DE genes, marker genes and cell outliers.}
 \usage{
-sc3_calc_biology.SingleCellExperiment(object, ks, regime)
+sc3_calc_biology.SingleCellExperiment(object, ks, regime, BPPARAM)
 
-\S4method{sc3_calc_biology}{SingleCellExperiment}(object, ks = NULL,
-  regime = NULL)
+\S4method{sc3_calc_biology}{SingleCellExperiment}(
+  object,
+  ks = NULL,
+  regime = NULL,
+  BPPARAM = BiocParallel::bpparam()
+)
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class}
@@ -24,35 +25,38 @@ Can also be a single integer.}
 \item{regime}{defines what biological analysis to perform. "marker" for
 marker genes, "de" for differentiall expressed genes and "outl" for outlier
 cells}
+
+\item{BPPARAM}{a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+including a seed for random number generator.}
 }
 \value{
 an object of \code{SingleCellExperiment} class
 }
 \description{
-This function calculates differentially expressed (DE) genes, marker genes 
+This function calculates differentially expressed (DE) genes, marker genes
 and cell outliers based on the consensus \code{SC3} clusterings.
 }
 \details{
-DE genes are calculated using \code{\link{get_de_genes}}. Results of the DE 
-analysis are saved as new columns in the 
-\code{featureData} slot of the input \code{object}. The column names correspond 
-to the adjusted \code{p-value}s of the genes and have the following format: 
+DE genes are calculated using \code{\link{get_de_genes}}. Results of the DE
+analysis are saved as new columns in the
+\code{featureData} slot of the input \code{object}. The column names correspond
+to the adjusted \code{p-value}s of the genes and have the following format:
 \code{sc3_k_de_padj}, where \code{k} is the number of clusters.
 
-Marker genes are calculated using \code{\link{get_marker_genes}}. 
-Results of the marker gene analysis are saved as three new 
-columns (for each \code{k}) to the 
-\code{featureData} slot of the input \code{object}. The column names correspond 
-to the \code{SC3} cluster labels, to the adjusted \code{p-value}s of the genes 
+Marker genes are calculated using \code{\link{get_marker_genes}}.
+Results of the marker gene analysis are saved as three new
+columns (for each \code{k}) to the
+\code{featureData} slot of the input \code{object}. The column names correspond
+to the \code{SC3} cluster labels, to the adjusted \code{p-value}s of the genes
 and to the area under the ROC curve
-and have the following format: \code{sc3_k_markers_clusts}, 
-\code{sc3_k_markers_padj} and \code{sc3_k_markers_auroc}, where \code{k} is 
+and have the following format: \code{sc3_k_markers_clusts},
+\code{sc3_k_markers_padj} and \code{sc3_k_markers_auroc}, where \code{k} is
 the number of clusters.
 
-Outlier cells are calculated using \code{\link{get_outl_cells}}. Results of the 
-cell outlier analysis are saved as new columns in the 
-\code{phenoData} slot of the input \code{object}. The column names correspond 
-to the \code{log2(outlier_score)} and have the following format: 
+Outlier cells are calculated using \code{\link{get_outl_cells}}. Results of the
+cell outlier analysis are saved as new columns in the
+\code{phenoData} slot of the input \code{object}. The column names correspond
+to the \code{log2(outlier_score)} and have the following format:
 \code{sc3_k_log2_outlier_score}, where \code{k} is the number of clusters.
 
 Additionally, \code{biology} item is added to the \code{sc3} slot and is set to
diff --git a/man/sc3_calc_consens.Rd b/man/sc3_calc_consens.Rd
index 7a7c4b1..443b8ec 100644
--- a/man/sc3_calc_consens.Rd
+++ b/man/sc3_calc_consens.Rd
@@ -1,21 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_calc_consens}
 \alias{sc3_calc_consens}
 \alias{sc3_calc_consens.SingleCellExperiment}
 \alias{sc3_calc_consens,}
 \alias{sc3_calc_consens,SingleCellExperiment-method}
-\alias{sc3_calc_consens,SingleCellExperiment-method}
-\alias{sc3_calc_consens}
 \title{Calculate consensus matrix.}
 \usage{
-sc3_calc_consens.SingleCellExperiment(object)
+sc3_calc_consens.SingleCellExperiment(object, BPPARAM)
 
-\S4method{sc3_calc_consens}{SingleCellExperiment}(object)
+\S4method{sc3_calc_consens}{SingleCellExperiment}(object, BPPARAM = BiocParallel::bpparam())
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class}
+
+\item{BPPARAM}{a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+including a seed for random number generator.}
 }
 \value{
 an object of \code{SingleCellExperiment} class
@@ -23,15 +23,15 @@ an object of \code{SingleCellExperiment} class
 \description{
 This function calculates consensus matrices based on the clustering solutions
 contained in the \code{kmeans} item of the \code{sc3} slot of the \code{metadata(object)}. It then
-creates and populates the \code{consensus} item of the \code{sc3} slot with 
+creates and populates the \code{consensus} item of the \code{sc3} slot with
 consensus matrices, their hierarchical clusterings in \code{hclust} objects,
-and Silhouette indeces of the clusters. It also removes the previously 
+and Silhouette indeces of the clusters. It also removes the previously
 calculated \code{kmeans} clusterings from
 the \code{sc3} slot, as they are not needed for further analysis.
 }
 \details{
 Additionally, it also adds new columns to the \code{colData} slot of the
 input \code{object}. The column names correspond to the consensus cell labels
-and have the following format: \code{sc3_k_clusters}, where \code{k} is the 
+and have the following format: \code{sc3_k_clusters}, where \code{k} is the
 number of clusters.
 }
diff --git a/man/sc3_calc_dists.Rd b/man/sc3_calc_dists.Rd
index 53337a0..6c439a6 100644
--- a/man/sc3_calc_dists.Rd
+++ b/man/sc3_calc_dists.Rd
@@ -1,21 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_calc_dists}
 \alias{sc3_calc_dists}
 \alias{sc3_calc_dists.SingleCellExperiment}
 \alias{sc3_calc_dists,}
 \alias{sc3_calc_dists,SingleCellExperiment-method}
-\alias{sc3_calc_dists,SingleCellExperiment-method}
-\alias{sc3_calc_dists}
 \title{Calculate distances between the cells.}
 \usage{
-sc3_calc_dists.SingleCellExperiment(object)
+sc3_calc_dists.SingleCellExperiment(object, BPPARAM)
 
-\S4method{sc3_calc_dists}{SingleCellExperiment}(object)
+\S4method{sc3_calc_dists}{SingleCellExperiment}(object, BPPARAM = BiocParallel::bpparam())
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class}
+
+\item{BPPARAM}{a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+including a seed for random number generator.}
 }
 \value{
 an object of \code{SingleCellExperiment} class
diff --git a/man/sc3_calc_transfs.Rd b/man/sc3_calc_transfs.Rd
index 04b8a37..5da93eb 100644
--- a/man/sc3_calc_transfs.Rd
+++ b/man/sc3_calc_transfs.Rd
@@ -1,32 +1,32 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_calc_transfs}
 \alias{sc3_calc_transfs}
 \alias{sc3_calc_transfs.SingleCellExperiment}
 \alias{sc3_calc_transfs,}
 \alias{sc3_calc_transfs,SingleCellExperiment-method}
-\alias{sc3_calc_transfs,SingleCellExperiment-method}
-\alias{sc3_calc_transfs}
 \title{Calculate transformations of the distance matrices.}
 \usage{
-sc3_calc_transfs.SingleCellExperiment(object)
+sc3_calc_transfs.SingleCellExperiment(object, BPPARAM)
 
-\S4method{sc3_calc_transfs}{SingleCellExperiment}(object)
+\S4method{sc3_calc_transfs}{SingleCellExperiment}(object, BPPARAM = BiocParallel::bpparam())
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class}
+
+\item{BPPARAM}{a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+including a seed for random number generator.}
 }
 \value{
 an object of \code{SingleCellExperiment} class
 }
 \description{
-This function transforms all \code{distances} items of the \code{sc3} slot of 
-the \code{metadata(object)} using either principal component analysis (PCA) 
+This function transforms all \code{distances} items of the \code{sc3} slot of
+the \code{metadata(object)} using either principal component analysis (PCA)
 or by calculating the eigenvectors of the associated graph Laplacian.
-The columns of the resulting matrices are then sorted in descending order 
-by their corresponding eigenvalues. The first \code{d} columns 
-(where \code{d = max(metadata(object)$sc3$n_dim)}) of each transformation are then 
+The columns of the resulting matrices are then sorted in descending order
+by their corresponding eigenvalues. The first \code{d} columns
+(where \code{d = max(metadata(object)$sc3$n_dim)}) of each transformation are then
 written to the \code{transformations} item of the \code{sc3} slot.
 Additionally, this function also removes the previously calculated \code{distances} from
 the \code{sc3} slot, as they are not needed for further analysis.
diff --git a/man/sc3_estimate_k.Rd b/man/sc3_estimate_k.Rd
index 46dc3e6..9b4a7ec 100644
--- a/man/sc3_estimate_k.Rd
+++ b/man/sc3_estimate_k.Rd
@@ -1,12 +1,9 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_estimate_k}
 \alias{sc3_estimate_k}
 \alias{sc3_estimate_k.SingleCellExperiment}
 \alias{sc3_estimate_k,SingleCellExperiment-method}
-\alias{sc3_estimate_k,SingleCellExperiment-method}
-\alias{sc3_estimate_k}
 \title{Estimate the optimal number of cluster \code{k} for a scRNA-Seq expression matrix}
 \usage{
 sc3_estimate_k.SingleCellExperiment(object)
diff --git a/man/sc3_export_results_xls.Rd b/man/sc3_export_results_xls.Rd
index 6de31ef..b6dca7b 100644
--- a/man/sc3_export_results_xls.Rd
+++ b/man/sc3_export_results_xls.Rd
@@ -1,17 +1,14 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_export_results_xls}
 \alias{sc3_export_results_xls}
 \alias{sc3_export_results_xls.SingleCellExperiment}
 \alias{sc3_export_results_xls,SingleCellExperiment-method}
-\alias{sc3_export_results_xls}
 \title{Write \code{SC3} results to Excel file}
 \usage{
 sc3_export_results_xls.SingleCellExperiment(object, filename)
 
-\S4method{sc3_export_results_xls}{SingleCellExperiment}(object,
-  filename = "sc3_results.xls")
+\S4method{sc3_export_results_xls}{SingleCellExperiment}(object, filename = "sc3_results.xls")
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class}
diff --git a/man/sc3_interactive.Rd b/man/sc3_interactive.Rd
index f2434b1..ad39254 100644
--- a/man/sc3_interactive.Rd
+++ b/man/sc3_interactive.Rd
@@ -1,13 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/ShinyMethods.R
-\docType{methods}
 \name{sc3_interactive}
 \alias{sc3_interactive}
 \alias{sc3_interactive.SingleCellExperiment}
 \alias{sc3_interactive,}
 \alias{sc3_interactive,SingleCellExperiment-method}
-\alias{sc3_interactive,SingleCellExperiment-method}
-\alias{sc3_interactive}
 \title{Opens \code{SC3} results in an interactive session in a web browser.}
 \usage{
 sc3_interactive.SingleCellExperiment(object)
diff --git a/man/sc3_kmeans.Rd b/man/sc3_kmeans.Rd
index aa48727..92776ee 100644
--- a/man/sc3_kmeans.Rd
+++ b/man/sc3_kmeans.Rd
@@ -1,30 +1,30 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_kmeans}
 \alias{sc3_kmeans}
 \alias{sc3_kmeans.SingleCellExperiment}
 \alias{sc3_kmeans,}
 \alias{sc3_kmeans,SingleCellExperiment-method}
-\alias{sc3_kmeans,SingleCellExperiment-method}
-\alias{sc3_kmeans}
 \title{\code{kmeans} clustering of cells.}
 \usage{
-sc3_kmeans.SingleCellExperiment(object, ks)
+sc3_kmeans.SingleCellExperiment(object, ks, BPPARAM)
 
-\S4method{sc3_kmeans}{SingleCellExperiment}(object, ks = NULL)
+\S4method{sc3_kmeans}{SingleCellExperiment}(object, ks = NULL, BPPARAM = BiocParallel::bpparam())
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class}
 
 \item{ks}{a continuous range of integers - the number of clusters \code{k} to be used for SC3 clustering.
 Can also be a single integer.}
+
+\item{BPPARAM}{a \code{\link[BiocParallel]{BiocParallelParam}} object specifying a type of parallelism and allocated resources,
+including a seed for random number generator.}
 }
 \value{
 an object of \code{SingleCellExperiment} class
 }
 \description{
-This function performs \code{\link[stats]{kmeans}} clustering of the matrices 
+This function performs \code{\link[stats]{kmeans}} clustering of the matrices
 contained in the \code{transformations} item of the \code{sc3} slot of the \code{metadata(object)}. It then
 creates and populates the following items of the \code{sc3} slot:
 \itemize{
diff --git a/man/sc3_plot_cluster_stability.Rd b/man/sc3_plot_cluster_stability.Rd
index 3bb2b41..e963375 100644
--- a/man/sc3_plot_cluster_stability.Rd
+++ b/man/sc3_plot_cluster_stability.Rd
@@ -1,13 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/PlotMethods.R
-\docType{methods}
 \name{sc3_plot_cluster_stability}
 \alias{sc3_plot_cluster_stability}
 \alias{sc3_plot_cluster_stability.SingleCellExperiment}
 \alias{sc3_plot_cluster_stability,}
 \alias{sc3_plot_cluster_stability,SingleCellExperiment-method}
-\alias{sc3_plot_cluster_stability,SingleCellExperiment-method}
-\alias{sc3_plot_cluster_stability}
 \title{Plot stability of the clusters}
 \usage{
 sc3_plot_cluster_stability.SingleCellExperiment(object, k)
diff --git a/man/sc3_plot_consensus.Rd b/man/sc3_plot_consensus.Rd
index b359342..8a9c3c3 100644
--- a/man/sc3_plot_consensus.Rd
+++ b/man/sc3_plot_consensus.Rd
@@ -1,19 +1,15 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/PlotMethods.R
-\docType{methods}
 \name{sc3_plot_consensus}
 \alias{sc3_plot_consensus}
 \alias{sc3_plot_consensus.SingleCellExperiment}
 \alias{sc3_plot_consensus,}
 \alias{sc3_plot_consensus,SingleCellExperiment-method}
-\alias{sc3_plot_consensus,SingleCellExperiment-method}
-\alias{sc3_plot_consensus}
 \title{Plot consensus matrix as a heatmap}
 \usage{
 sc3_plot_consensus.SingleCellExperiment(object, k, show_pdata)
 
-\S4method{sc3_plot_consensus}{SingleCellExperiment}(object, k,
-  show_pdata = NULL)
+\S4method{sc3_plot_consensus}{SingleCellExperiment}(object, k, show_pdata = NULL)
 }
 \arguments{
 \item{object}{an object of 'SingleCellExperiment' class}
diff --git a/man/sc3_plot_de_genes.Rd b/man/sc3_plot_de_genes.Rd
index 8e7d97a..bff4d5f 100644
--- a/man/sc3_plot_de_genes.Rd
+++ b/man/sc3_plot_de_genes.Rd
@@ -1,19 +1,15 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/PlotMethods.R
-\docType{methods}
 \name{sc3_plot_de_genes}
 \alias{sc3_plot_de_genes}
 \alias{sc3_plot_de_genes.SingleCellExperiment}
 \alias{sc3_plot_de_genes,}
 \alias{sc3_plot_de_genes,SingleCellExperiment-method}
-\alias{sc3_plot_de_genes,SingleCellExperiment-method}
-\alias{sc3_plot_de_genes}
 \title{Plot expression of DE genes of the clusters identified by \code{SC3} as a heatmap}
 \usage{
 sc3_plot_de_genes.SingleCellExperiment(object, k, p.val, show_pdata)
 
-\S4method{sc3_plot_de_genes}{SingleCellExperiment}(object, k, p.val = 0.01,
-  show_pdata = NULL)
+\S4method{sc3_plot_de_genes}{SingleCellExperiment}(object, k, p.val = 0.01, show_pdata = NULL)
 }
 \arguments{
 \item{object}{an object of 'SingleCellExperiment' class}
diff --git a/man/sc3_plot_expression.Rd b/man/sc3_plot_expression.Rd
index b728c28..6b91783 100644
--- a/man/sc3_plot_expression.Rd
+++ b/man/sc3_plot_expression.Rd
@@ -1,19 +1,15 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/PlotMethods.R
-\docType{methods}
 \name{sc3_plot_expression}
 \alias{sc3_plot_expression}
 \alias{sc3_plot_expression.SingleCellExperiment}
 \alias{sc3_plot_expression,}
 \alias{sc3_plot_expression,SingleCellExperiment-method}
-\alias{sc3_plot_expression,SingleCellExperiment-method}
-\alias{sc3_plot_expression}
 \title{Plot expression matrix used for SC3 clustering as a heatmap}
 \usage{
 sc3_plot_expression.SingleCellExperiment(object, k, show_pdata)
 
-\S4method{sc3_plot_expression}{SingleCellExperiment}(object, k,
-  show_pdata = NULL)
+\S4method{sc3_plot_expression}{SingleCellExperiment}(object, k, show_pdata = NULL)
 }
 \arguments{
 \item{object}{an object of 'SingleCellExperiment' class}
diff --git a/man/sc3_plot_markers.Rd b/man/sc3_plot_markers.Rd
index 50a0028..29c203e 100644
--- a/man/sc3_plot_markers.Rd
+++ b/man/sc3_plot_markers.Rd
@@ -1,19 +1,15 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/PlotMethods.R
-\docType{methods}
 \name{sc3_plot_markers}
 \alias{sc3_plot_markers}
 \alias{sc3_plot_markers.SingleCellExperiment}
 \alias{sc3_plot_markers,}
 \alias{sc3_plot_markers,SingleCellExperiment-method}
-\alias{sc3_plot_markers,SingleCellExperiment-method}
-\alias{sc3_plot_markers}
 \title{Plot expression of marker genes identified by \code{SC3} as a heatmap.}
 \usage{
 sc3_plot_markers.SingleCellExperiment(object, k, auroc, p.val, show_pdata)
 
-\S4method{sc3_plot_markers}{SingleCellExperiment}(object, k, auroc = 0.85,
-  p.val = 0.01, show_pdata = NULL)
+\S4method{sc3_plot_markers}{SingleCellExperiment}(object, k, auroc = 0.85, p.val = 0.01, show_pdata = NULL)
 }
 \arguments{
 \item{object}{an object of 'SingleCellExperiment' class}
diff --git a/man/sc3_plot_silhouette.Rd b/man/sc3_plot_silhouette.Rd
index 0f0f0c9..76467f2 100644
--- a/man/sc3_plot_silhouette.Rd
+++ b/man/sc3_plot_silhouette.Rd
@@ -1,13 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/PlotMethods.R
-\docType{methods}
 \name{sc3_plot_silhouette}
 \alias{sc3_plot_silhouette}
 \alias{sc3_plot_silhouette.SingleCellExperiment}
 \alias{sc3_plot_silhouette,}
 \alias{sc3_plot_silhouette,SingleCellExperiment-method}
-\alias{sc3_plot_silhouette,SingleCellExperiment-method}
-\alias{sc3_plot_silhouette}
 \title{Plot silhouette indexes of the cells}
 \usage{
 sc3_plot_silhouette.SingleCellExperiment(object, k)
diff --git a/man/sc3_prepare.Rd b/man/sc3_prepare.Rd
index c24bf56..0b15fee 100644
--- a/man/sc3_prepare.Rd
+++ b/man/sc3_prepare.Rd
@@ -1,63 +1,72 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_prepare}
 \alias{sc3_prepare}
 \alias{sc3_prepare.SingleCellExperiment}
 \alias{sc3_prepare,SingleCellExperiment-method}
-\alias{sc3_prepare,SingleCellExperiment-method}
-\alias{sc3_prepare}
 \title{Prepare the \code{SingleCellExperiment} object for \code{SC3} clustering.}
 \usage{
-sc3_prepare.SingleCellExperiment(object, gene_filter, pct_dropout_min,
-  pct_dropout_max, d_region_min, d_region_max, svm_num_cells, svm_train_inds,
-  svm_max, n_cores, kmeans_nstart, kmeans_iter_max, rand_seed)
+sc3_prepare.SingleCellExperiment(
+  object,
+  gene_filter,
+  pct_dropout_min,
+  pct_dropout_max,
+  d_region_min,
+  d_region_max,
+  svm_num_cells,
+  svm_train_inds,
+  svm_max,
+  kmeans_nstart,
+  kmeans_iter_max
+)
 
-\S4method{sc3_prepare}{SingleCellExperiment}(object, gene_filter = TRUE,
-  pct_dropout_min = 10, pct_dropout_max = 90, d_region_min = 0.04,
-  d_region_max = 0.07, svm_num_cells = NULL, svm_train_inds = NULL,
-  svm_max = 5000, n_cores = NULL, kmeans_nstart = NULL,
-  kmeans_iter_max = 1e+09, rand_seed = 1)
+\S4method{sc3_prepare}{SingleCellExperiment}(
+  object,
+  gene_filter = TRUE,
+  pct_dropout_min = 10,
+  pct_dropout_max = 90,
+  d_region_min = 0.04,
+  d_region_max = 0.07,
+  svm_num_cells = NULL,
+  svm_train_inds = NULL,
+  svm_max = 5000,
+  kmeans_nstart = NULL,
+  kmeans_iter_max = 1e+09
+)
 }
 \arguments{
 \item{object}{an object of \code{SingleCellExperiment} class.}
 
-\item{gene_filter}{a boolen variable which defines whether to perform gene 
+\item{gene_filter}{a boolen variable which defines whether to perform gene
 filtering before SC3 clustering.}
 
-\item{pct_dropout_min}{if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than 
+\item{pct_dropout_min}{if \code{gene_filter = TRUE}, then genes with percent of dropouts smaller than
 \code{pct_dropout_min} are filtered out before clustering.}
 
-\item{pct_dropout_max}{if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than 
+\item{pct_dropout_max}{if \code{gene_filter = TRUE}, then genes with percent of dropouts larger than
 \code{pct_dropout_max} are filtered out before clustering.}
 
-\item{d_region_min}{defines the minimum number of eigenvectors used for 
+\item{d_region_min}{defines the minimum number of eigenvectors used for
 kmeans clustering as a fraction of the total number of cells. Default is \code{0.04}.
 See \code{SC3} paper for more details.}
 
-\item{d_region_max}{defines the maximum number of eigenvectors used for 
+\item{d_region_max}{defines the maximum number of eigenvectors used for
 kmeans clustering as a fraction of the total number of cells. Default is \code{0.07}.
 See \code{SC3} paper for more details.}
 
-\item{svm_num_cells}{number of randomly selected training cells to be used 
+\item{svm_num_cells}{number of randomly selected training cells to be used
 for SVM prediction. The default is \code{NULL}.}
 
-\item{svm_train_inds}{a numeric vector defining indeces of training cells 
+\item{svm_train_inds}{a numeric vector defining indeces of training cells
 that should be used for SVM training. The default is \code{NULL}.}
 
 \item{svm_max}{define the maximum number of cells below which SVM is not run.}
 
-\item{n_cores}{defines the number of cores to be used on the user's machine. If not set, `SC3` will use all but one cores of your machine.}
-
-\item{kmeans_nstart}{nstart parameter passed to \code{\link[stats]{kmeans}} function. Default is 
+\item{kmeans_nstart}{nstart parameter passed to \code{\link[stats]{kmeans}} function. Default is
 \code{1000} for up to \code{2000} cells and \code{50} for more than \code{2000} cells.}
 
-\item{kmeans_iter_max}{iter.max parameter passed to \code{\link[stats]{kmeans}} 
+\item{kmeans_iter_max}{iter.max parameter passed to \code{\link[stats]{kmeans}}
 function. Default is \code{1e+09}.}
-
-\item{rand_seed}{sets the seed of the random number generator. \code{SC3} is a stochastic
-method, so setting the \code{rand_seed} to a fixed values can be used for reproducibility
-purposes.}
 }
 \value{
 an object of \code{SingleCellExperiment} class
@@ -70,11 +79,9 @@ creates and populates the following items of the \code{sc3} slot of the \code{me
   \item \code{kmeans_nstart} - the same as the \code{kmeans_nstart} argument.
   \item \code{n_dim} - contains numbers of the number of eigenvectors to be used
   in \code{\link[stats]{kmeans}} clustering.
-  \item \code{rand_seed} - the same as the \code{rand_seed} argument.
-  \item \code{svm_train_inds} - if SVM is used this item contains indexes of the 
+  \item \code{svm_train_inds} - if SVM is used this item contains indexes of the
   training cells to be used for SC3 clustering and further SVM prediction.
   \item \code{svm_study_inds} - if SVM is used this item contains indexes of the
    cells to be predicted by SVM.
-  \item \code{n_cores} - the same as the \code{n_cores} argument.
 }
 }
diff --git a/man/sc3_run_svm.Rd b/man/sc3_run_svm.Rd
index 11bd707..dc8a961 100644
--- a/man/sc3_run_svm.Rd
+++ b/man/sc3_run_svm.Rd
@@ -1,13 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/CoreMethods.R
-\docType{methods}
 \name{sc3_run_svm}
 \alias{sc3_run_svm}
 \alias{sc3_run_svm.SingleCellExperiment}
 \alias{sc3_run_svm,}
 \alias{sc3_run_svm,SingleCellExperiment-method}
-\alias{sc3_run_svm,SingleCellExperiment-method}
-\alias{sc3_run_svm}
 \title{Run the hybrid \code{SVM} approach.}
 \usage{
 sc3_run_svm.SingleCellExperiment(object, ks)
@@ -25,13 +22,13 @@ an object of \code{SingleCellExperiment} class
 }
 \description{
 This method parallelize \code{SVM} prediction for each \code{k} (the number
-of clusters). Namely, for each \code{k}, \code{\link{support_vector_machines}} 
+of clusters). Namely, for each \code{k}, \code{\link{support_vector_machines}}
 function is utilized to predict the labels of study cells. Training cells are
 selected using \code{svm_train_inds} item of the \code{sc3} slot of the
 \code{metadata(object)}.
 }
 \details{
-Results are written to the \code{sc3_k_clusters} columns to the 
-\code{colData} slot of the input \code{object}, where \code{k} is the 
+Results are written to the \code{sc3_k_clusters} columns to the
+\code{colData} slot of the input \code{object}, where \code{k} is the
 number of clusters.
 }
diff --git a/man/yan.Rd b/man/yan.Rd
index e02a0ee..a0db34d 100644
--- a/man/yan.Rd
+++ b/man/yan.Rd
@@ -4,7 +4,9 @@
 \name{yan}
 \alias{yan}
 \title{Single cell RNA-Seq data extracted from a publication by Yan et al.}
-\format{An object of class \code{data.frame} with 20214 rows and 90 columns.}
+\format{
+An object of class \code{data.frame} with 20214 rows and 90 columns.
+}
 \source{
 \url{http://dx.doi.org/10.1038/nsmb.2660}
 
diff --git a/vignettes/SC3.Rmd b/vignettes/SC3.Rmd
index 40eec2d..aa356bf 100644
--- a/vignettes/SC3.Rmd
+++ b/vignettes/SC3.Rmd
@@ -54,7 +54,7 @@ sce <- SingleCellExperiment(
     assays = list(
         counts = as.matrix(yan),
         logcounts = log2(as.matrix(yan) + 1)
-    ), 
+    ),
     colData = ann
 )
 
@@ -70,6 +70,23 @@ sce <- runPCA(sce)
 plotPCA(sce, colour_by = "cell_type1")
 ```
 
+## Speeding up the computations
+
+`SC3` is using the [BiocParallel](https://bioconductor.org/packages/release/bioc/html/BiocParallel.html) package for parallel computations.
+To utilize its capabilities, you have to create an object of class `BiocParallelParam`, which defines the parallel backend, its allocated
+resources, and also a seed for random number generator. The most common is `SnowParam()`:
+```{r}
+library(BiocParallel)
+BPPARAM <- SnowParam(workers = 4, type = "SOCK", RNGseed = 1, progressbar = TRUE)
+bpstart(BPPARAM)
+BiocParallel::register(BPPARAM)
+```
+
+Instead of calling the `register()` function you can also directly pass `BPPARAM` to `SC3` methods which support it.
+
+> `type = "SOCK"` works on all platforms, because it uses external R processes to distribute the parallel computations.
+>  On the other hand, `type = "FORK"` works only on Unix platforms, and could be faster in exchange for increased memory consumption.
+
 ## Run SC3
 
 If you would like to explore clustering of your data in the range of `k`s (the number of clusters) from 2 to 4, you just need to run the main `sc3` method and define the range of `k`s using the `ks` parameter (here we also ask `SC3` to calculate biological features based on the identified cell clusters):
@@ -77,8 +94,6 @@ If you would like to explore clustering of your data in the range of `k`s (the n
 sce <- sc3(sce, ks = 2:4, biology = TRUE)
 ```
 
-> By default `SC3` will use all but one cores of your machine. You can manually set the number of cores to be used by setting the `n_cores` parameter in the `sc3` call.
-
 To quickly and easily explore the `SC3` solutions using an interactive Shiny application use the following method:
 ```{r, eval=FALSE}
 sc3_interactive(sce)
@@ -104,8 +119,8 @@ Additionally, having `SC3` results stored in the same slot makes it possible to
 ```{r}
 sce <- runPCA(sce)
 plotPCA(
-    sce, 
-    colour_by = "sc3_3_clusters", 
+    sce,
+    colour_by = "sc3_3_clusters",
     size_by = "sc3_3_log2_outlier_score"
 )
 ```
@@ -123,7 +138,7 @@ Because the biological features were also calculated for each `k`, one can find
 
 # Number of Сells
 
-The default settings of `SC3` allow to cluster (using a single `k`) a dataset of 2,000 cells in about 20-30 minutes. 
+The default settings of `SC3` allow to cluster (using a single `k`) a dataset of 2,000 cells in about 20-30 minutes.
 
 For datasets with more than 2,000 cells `SC3` automatically adjusts some of its parameters (see below). This allows to cluster a dataset of 5,000 cells in about 20-30 minutes. The parameters can also be manually adjusted for datasets with any number of cells.
 
@@ -143,11 +158,11 @@ sc3_plot_consensus(sce, k = 3)
 It is also possible to annotate cells (columns of the consensus matrix) with any column of the `colData` slot of the `sce` object.
 ```{r, fig.height=6, fig.width=8}
 sc3_plot_consensus(
-    sce, k = 3, 
+    sce, k = 3,
     show_pdata = c(
-        "cell_type1", 
+        "cell_type1",
         "log10_total_features",
-        "sc3_3_clusters", 
+        "sc3_3_clusters",
         "sc3_3_log2_outlier_score"
     )
 )
@@ -170,11 +185,11 @@ sc3_plot_expression(sce, k = 3)
 It is also possible to annotate cells (columns of the expression matrix) with any column of the `colData` slot of the `sce` object.
 ```{r, fig.height=6, fig.width=8}
 sc3_plot_expression(
-    sce, k = 3, 
+    sce, k = 3,
     show_pdata = c(
-        "cell_type1", 
+        "cell_type1",
         "log10_total_features",
-        "sc3_3_clusters", 
+        "sc3_3_clusters",
         "sc3_3_log2_outlier_score"
     )
 )
@@ -197,11 +212,11 @@ sc3_plot_de_genes(sce, k = 3)
 It is also possible to annotate cells (columns of the matrix containing DE genes) with any column of the `colData` slot of the `sce` object.
 ```{r, fig.height=9, fig.width=8}
 sc3_plot_de_genes(
-    sce, k = 3, 
+    sce, k = 3,
     show_pdata = c(
-        "cell_type1", 
+        "cell_type1",
         "log10_total_features",
-        "sc3_3_clusters", 
+        "sc3_3_clusters",
         "sc3_3_log2_outlier_score"
     )
 )
@@ -217,11 +232,11 @@ sc3_plot_markers(sce, k = 3)
 It is also possible to annotate cells (columns of the matrix containing marker genes) with any column of the `colData` slot of the `sce` object.
 ```{r, fig.height=6, fig.width=8}
 sc3_plot_markers(
-    sce, k = 3, 
+    sce, k = 3,
     show_pdata = c(
-        "cell_type1", 
+        "cell_type1",
         "log10_total_features",
-        "sc3_3_clusters", 
+        "sc3_3_clusters",
         "sc3_3_log2_outlier_score"
     )
 )
@@ -243,15 +258,13 @@ Let us go through each of them independently.
 
 ## `sc3_prepare`
 
-We start with `sc3_prepare`. This method prepares an object of `sce` class for `SC3` clustering. This method also defines all parameters needed for clustering and stores them in the `sc3` slot. The parameters have their own defaults but can be manually changed. For more information on the parameters please use `?sc3_prepare`. 
+We start with `sc3_prepare`. This method prepares an object of `sce` class for `SC3` clustering. This method also defines all parameters needed for clustering and stores them in the `sc3` slot. The parameters have their own defaults but can be manually changed. For more information on the parameters please use `?sc3_prepare`.
 
 ```{r}
 sce <- sc3_prepare(sce)
 str(metadata(sce)$sc3)
 ```
 
-> By default `SC3` will use all but one cores of your machine. You can manually set the number of cores to be used by setting the `n_cores` parameter in the `sc3_prepare` call.
-
 ## _(optional)_ `sc3_estimate_k`
 
 When the `sce` object is prepared for clustering, `SC3` can also estimate the optimal number of clusters `k` in the dataset. `SC3` utilizes the Tracy-Widom theory on random matrices to estimate `k`. `sc3_estimate_k` method creates and populates the following items of the `sc3` slot:
@@ -274,7 +287,7 @@ names(metadata(sce)$sc3$distances)
 
 ## `sc3_calc_transfs`
 
-Next the distance matrices are transformed using PCA and graph Laplacian. Method `sc3_calc_transfs` calculates transforamtions of the distance matrices contained in 
+Next the distance matrices are transformed using PCA and graph Laplacian. Method `sc3_calc_transfs` calculates transforamtions of the distance matrices contained in
 the `distances` item of the `sc3` slot. It then creates and populates the following items of the `sc3` slot:
 
 * `transformations` - contains a list of transformations of the distance matrices corresponding to PCA and graph Laplacian transformations.