Update run script for musAdPrF

r.scripts/sc-TissueMapper_RUN.R

@@ -27,9 +27,9 @@ scFolders()
 
 sc10x <- scLoad(p=project.name,cellranger=3,ref="mm10")
 
-lg=500
-hg=3000
-hm=0.05
+lg=250
+hg=2000
+hm=0.1
 results <- scQC(sc10x,lg=lg,hg=hg,hm=hm,sub=FALSE,sp="mu")
 sc10x <- results[[1]]
 counts.cell.raw <- results[[2]]
@@ -41,53 +41,53 @@ rm(lg)
 rm(hg)
 rm(hm)
 
-results <- scCellCycle(sc10x,sub=FALSE,sp="mu")
-sc10x <- results[[1]]
-genes.s <- results[[2]]
-genes.g2m <- results[[3]]
-rm(results)
+# results <- scCellCycle(sc10x,sub=FALSE,sp="mu")
+# sc10x <- results[[1]]
+# genes.s <- results[[2]]
+# genes.g2m <- results[[3]]
+# rm(results)
 
 sc10x.l <- list()
-sc10x.l[["Pd"]] <- subset(sc10x,subset= Donor=="Donor")
-sc10x.l[["Pb"]] <- subset(sc10x,subset= BPH=="BPH")
-sc10x.l[["Pc"]] <- subset(sc10x,subset= Cancer=="Cancer")
+sc10x.l[["mu1"]] <- subset(sc10x,subset= samples=="musAd001_PrF")
+sc10x.l[["mu2"]] <- subset(sc10x,subset= samples=="musAd002_PrF")
+sc10x.l[["mu3"]] <- subset(sc10x,subset= samples=="musAd003_PrF_St")
 
 sc10x <- scCCA(sc10x.l)
 rm(sc10x.l)
 sc10x@project.name <- project.name
 
-#gc()
-sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito","S.Score","G2M.Score"),do.par=TRUE,num.cores=45,verbose=FALSE)
-#sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),do.par=TRUE,num.cores=45,verbose=FALSE)
-#gc()
+# gc()
+# sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito","S.Score","G2M.Score"),do.par=TRUE,num.cores=45,verbose=FALSE)
+sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),do.par=TRUE,num.cores=45,verbose=FALSE)
+# gc()
 
 results <- scPC(sc10x,pc=50,hpc=0.9,file="pre.stress",print="2",cca=TRUE)
 sc10x <- results[[1]]
 pc.use.prestress <- results[[2]]
 rm(results)
 
-sc10x <- scCluster(sc10x,res=0.5,red="pca",dim=pc.use.prestress,print="2",folder="pre.stress")
+# sc10x <- scCluster(sc10x,res=0.5,red="pca",dim=pc.use.prestress,print="2",folder="pre.stress")
 
-genes.stress <- read_delim("./genesets/genes.deg.Stress.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
-genes.stress <- genes.stress[1]
-colnames(genes.stress) <- "scDWS.Stress"
+# genes.stress <- read_delim("./genesets/genes.deg.Stress.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+# genes.stress <- genes.stress[1]
+# colnames(genes.stress) <- "scDWS.Stress"
 
-results <- scScore(sc10x,score="Stress",geneset=as.list(genes.stress),cut.pt=0.75,anchor=c("EGR1","FOS","JUN"))
-sc10x.preStress <- results[[1]]
-sc10x <- results[[2]]
-rm(results)
+# results <- scScore(sc10x,score="Stress",geneset=as.list(genes.stress),cut.pt=0.75,anchor=c("EGR1","FOS","JUN"))
+# sc10x.preStress <- results[[1]]
+# sc10x <- results[[2]]
+# rm(results)
 
-#gc()
-sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito","S.Score","G2M.Score"),do.par=TRUE,num.cores=45,verbose=FALSE)
-#sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),do.par=TRUE,num.cores=45,verbose=FALSE)
-#gc()
+# gc()
+# sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito","S.Score","G2M.Score"),do.par=TRUE,num.cores=45,verbose=FALSE)
+# sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),do.par=TRUE,num.cores=45,verbose=FALSE)
+# gc()
 
-results <- scPC(sc10x,pc=50,hpc=0.9,file="post.stress",print="2",cca=TRUE)
-sc10x <- results[[1]]
-pc.use.poststress <- results[[2]]
-rm(results)
+# results <- scPC(sc10x,pc=50,hpc=0.9,file="post.stress",print="2",cca=TRUE)
+# sc10x <- results[[1]]
+# pc.use.poststress <- results[[2]]
+# rm(results)
 
 res <- c(seq(0.1,0.5,0.1),0.75,seq(1,5,1))
-sc10x <- scCluster(sc10x,res=res,red="pca",dim=pc.use.poststress,print="2",folder="ALL")
+sc10x <- scCluster(sc10x,res=res,red="pca",dim=pc.use.prestress,print="2",folder="ALL")
 
 save(sc10x,file=paste0("./analysis/sc10x.raw.rda"))