Fixed the sparse matrix issue

DESCRIPTION

@@ -15,15 +15,34 @@ Authors@R: c(person("Vladimir", "Kiselev",
              role=c("aut")))
 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
+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,
+    Matrix
 Depends: R(>= 3.3)
 LinkingTo: Rcpp, RcppArmadillo
 LazyData: TRUE
-RoxygenNote: 6.0.1
+RoxygenNote: 6.1.1
 Suggests: knitr, rmarkdown, mclust, scater
 VignetteBuilder: knitr
 biocViews: SingleCell, Software, Classification, Clustering, DimensionReduction, 

NAMESPACE

@@ -6,6 +6,7 @@ export(get_marker_genes)
 export(get_outl_cells)
 export(get_processed_dataset)
 export(norm_laplacian)
+export(organise_marker_genes)
 export(reindex_clusters)
 export(sc3)
 export(sc3_calc_biology)
@@ -54,6 +55,7 @@ importFrom(ggplot2,ylim)
 importFrom(graphics,plot)
 importFrom(methods,as)
 importFrom(methods,new)
+importFrom(parallel,clusterCall)
 importFrom(parallel,detectCores)
 importFrom(parallel,makeCluster)
 importFrom(parallel,stopCluster)

R/CoreFunctions.R

@@ -30,6 +30,9 @@
 #' @importFrom Rcpp sourceCpp
 #'
 calculate_distance <- function(data, method) {
+    if(class(data) == "dgCMatrix"){
+      data = as.matrix(data)
+    }
     return(if (method == "spearman") {
         as.matrix(1 - cor(data, method = "spearman"))
     } else if (method == "pearson") {

R/CoreMethods.R

@@ -149,7 +149,7 @@ sc3_prepare.SingleCellExperiment <- function(object, gene_filter, pct_dropout_mi
     f_data <- rowData(object)
     f_data$sc3_gene_filter <- TRUE
     if (gene_filter) {
-        dropouts <- rowSums(counts(object) == 0)/ncol(object)*100
+        dropouts <- Matrix::rowSums(counts(object) == 0)/ncol(object)*100
         if(!is.null(isSpike(object))) {
             f_data$sc3_gene_filter <- dropouts < pct_dropout_max & dropouts > pct_dropout_min & !isSpike(object)
         } else {
@@ -291,7 +291,7 @@ setMethod("sc3_estimate_k", signature(object = "SingleCellExperiment"), sc3_esti
 #' 
 #' @importFrom doRNG %dorng%
 #' @importFrom foreach foreach %dopar%
-#' @importFrom parallel makeCluster stopCluster
+#' @importFrom parallel makeCluster stopCluster clusterCall
 #' @importFrom doParallel registerDoParallel
 sc3_calc_dists.SingleCellExperiment <- function(object) {
     dataset <- get_processed_dataset(object)
@@ -317,6 +317,9 @@ sc3_calc_dists.SingleCellExperiment <- function(object) {
     cl <- parallel::makeCluster(n_cores, outfile = "")
     doParallel::registerDoParallel(cl, cores = n_cores)
 
+    ## pass .libPaths to workers
+    parallel::clusterCall(cl, function(x) {.libPaths(.Library); .libPaths(x)}, .libPaths())
+
     # calculate distances in parallel
     dists <- foreach::foreach(i = distances) %dorng% {
         try({
@@ -358,7 +361,7 @@ setMethod("sc3_calc_dists", signature(object = "SingleCellExperiment"), sc3_calc
 #' 
 #' @importFrom doRNG %dorng%
 #' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
+#' @importFrom parallel makeCluster stopCluster clusterCall
 #' @importFrom doParallel registerDoParallel
 sc3_calc_transfs.SingleCellExperiment <- function(object) {
     dists <- metadata(object)$sc3$distances
@@ -388,6 +391,9 @@ sc3_calc_transfs.SingleCellExperiment <- function(object) {
     cl <- parallel::makeCluster(n_cores, outfile = "")
     doParallel::registerDoParallel(cl, cores = n_cores)
 
+    ## pass .libPaths to workers
+    parallel::clusterCall(cl, function(x) {.libPaths(.Library); .libPaths(x)}, .libPaths())
+
     # calculate the 6 distinct transformations in parallel
     transfs <- foreach::foreach(i = 1:nrow(hash.table)) %dorng% {
         try({
@@ -431,7 +437,7 @@ setMethod("sc3_calc_transfs", signature(object = "SingleCellExperiment"), sc3_ca
 #' 
 #' @importFrom doRNG %dorng%
 #' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
+#' @importFrom parallel makeCluster stopCluster clusterCall
 #' @importFrom doParallel registerDoParallel
 #' @importFrom utils setTxtProgressBar txtProgressBar
 #' @importFrom stats kmeans
@@ -464,6 +470,9 @@ sc3_kmeans.SingleCellExperiment <- function(object, ks) {
     cl <- parallel::makeCluster(n_cores, outfile = "")
     doParallel::registerDoParallel(cl, cores = n_cores)
 
+    ## pass .libPaths to workers
+    parallel::clusterCall(cl, function(x) {.libPaths(.Library); .libPaths(x)}, .libPaths())
+
     pb <- utils::txtProgressBar(min = 1, max = nrow(hash.table), style = 3)
 
     # calculate the 6 distinct transformations in parallel
@@ -515,7 +524,7 @@ setMethod("sc3_kmeans", signature(object = "SingleCellExperiment"), sc3_kmeans.S
 #' 
 #' @importFrom doRNG %dorng%
 #' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
+#' @importFrom parallel makeCluster stopCluster clusterCall
 #' @importFrom doParallel registerDoParallel
 #' @import cluster
 #' @importFrom stats hclust dist as.dist
@@ -545,6 +554,9 @@ sc3_calc_consens.SingleCellExperiment <- function(object) {
     cl <- parallel::makeCluster(n_cores, outfile = "")
     doParallel::registerDoParallel(cl, cores = n_cores)
 
+    ## pass .libPaths to workers
+    parallel::clusterCall(cl, function(x) {.libPaths(.Library); .libPaths(x)}, .libPaths())
+
     cons <- foreach::foreach(i = ks) %dorng% {
         try({
             d <- k.means[grep(paste0("_", i, "_"), names(k.means))]
@@ -644,7 +656,7 @@ setMethod("sc3_calc_consens", signature(object = "SingleCellExperiment"), sc3_ca
 #' 
 #' @importFrom doRNG %dorng%
 #' @importFrom foreach foreach
-#' @importFrom parallel makeCluster stopCluster
+#' @importFrom parallel makeCluster stopCluster clusterCall
 #' @importFrom doParallel registerDoParallel
 #' @importFrom methods as
 sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
@@ -695,6 +707,9 @@ sc3_calc_biology.SingleCellExperiment <- function(object, ks, regime) {
     cl <- parallel::makeCluster(n_cores, outfile = "")
     doParallel::registerDoParallel(cl, cores = n_cores)
 
+    ## pass .libPaths to workers
+    parallel::clusterCall(cl, function(x) {.libPaths(.Library); .libPaths(x)}, .libPaths())
+
     biol <- foreach::foreach(i = 1:nrow(hash.table)) %dorng% {
         try({
             get_biolgy(dataset, clusts[, paste0("sc3_", hash.table[i, 1], "_clusters")], hash.table[i, 2])