Replace sc_QC parameters as seurat subset has error reading variables inside functions

r.scripts/sc-TissueMapper_RUN.R

@@ -26,13 +26,19 @@ scFolders()
 
 sc10x <- scLoad(p=project.name)
 
-results <- scQC(sc10x,lg=500,hg=2500,hm=0.1,sub=FALSE)
+lg=500
+hg=2500
+hm=0.1
+results <- scQC(sc10x,lg=lg,hg=hg,hm=hm,sub=FALSE)
 sc10x <- results[[1]]
 counts.cell.raw <- results[[2]]
 counts.gene.raw <- results[[3]]
 counts.cell.filtered <- results[[4]]
 counts.gene.filtered <- results[[5]]
 rm(results)
+rm(lg)
+rm(hg)
+rm(hm)
 
 results <- scCellCycle(sc10x,sub=FALSE)
 sc10x <- results[[1]]

r.scripts/sc-TissueMapper_functions.R

@@ -146,9 +146,9 @@ scQC <- function(sc10x,lg=500,hg=2500,hm=0.1,sub=FALSE){
   counts.gene.raw <- nrow(GetAssayData(object=sc10x,slot="counts"))
   
   #Filter/normalize data
-  sc10x.sub <- subset(x=sc10x,subset=nFeature_RNA > lg)
-  sc10x.sub <- subset(x=sc10x.sub,subset=nFeature_RNA < hg)
-  sc10x.sub <- subset(x=sc10x.sub,subset=percent.mito < hm)
+  sc10x.sub <- subset(x=sc10x,subset= nFeature_RNA > lg)
+  sc10x.sub <- subset(x=sc10x.sub,subset= nFeature_RNA < hg)
+  sc10x.sub <- subset(x=sc10x.sub,subset= percent.mito < hm)
   sc10x.sub <- NormalizeData(object=sc10x.sub,normalization.method="LogNormalize",scale.factor=10000,verbose=FALSE)
   
   #Plot filtered stats