diff --git a/sc_workflow/sc_quality_check.R b/sc_workflow/sc_quality_check.R
index 18d043819b4b83bed3fe6919db7cbb0ca609c60b..04da439d76b19bb753b270d4f40d7bdde1a298a2 100644
--- a/sc_workflow/sc_quality_check.R
+++ b/sc_workflow/sc_quality_check.R
@@ -13,7 +13,7 @@ Quality control and filtering of scRNAseq data
 Usage: sc_quality_check.R <folder_with_star_counts_matrices_and_design>
 '
 
-# commandArgs <- function(x) c("metrices")
+# commandArgs <- function(x) c("metrices_subsetted")
 opts = docopt(doc, commandArgs(TRUE))
 
 
@@ -93,11 +93,10 @@ sce <- lapply(setNames(names(sce), names(sce)), function(x) {
 })
 
 #'<br><br>
-#' ## Check for sequencing saturation
+#' ## Check sequencing saturation
 #' The following saturation plot of the invidivudal cells is based on the sequencing depth of per cell as well as higher
 #' and lower simulated sequencing depths. For this plot all features with a counts > 0 were taken into account.
 lapply(setNames(names(sce), names(sce)), function(x){
-    x = "SC2"
     dat <- readData(data = counts(sce[[x]]), factors = colData(sce[[x]]))
     mysaturation = dat(dat, k = 0, ndepth = 7, type = "saturation")
     sat_data <- dat2save(mysaturation)
@@ -117,7 +116,7 @@ lapply(setNames(names(sce), names(sce)), function(x){
 
 
 #'<br><br>
-#' ## Check cell's library sizes
+#' ## Check library size
 #' The library size is defined by the sum of all counts per cell and corresponds to the number of reads mapped to the reference genome
 #' <br>
 lapply(names(sce), function(x){
@@ -130,7 +129,7 @@ lapply(names(sce), function(x){
 # grid.arrange(grobs = ., nrow = plot_rows, heights=unit(0.5, "npc"))
 
 #' <br><br>
-#' ## Check cell's number of expressed genes
+#' ## Check number of expressed genes
 #' The number of expressed genes corresponds to the number of genes per cell with an actual count (i.e. > 0)
 #' <br>
 lapply(names(sce), function(x){