Fix DPrF analysis to use correct CCA code and make separate r.scripts for 2...

Fix DPrF analysis to use correct CCA code and make separate r.scripts for 2 and 3 patient aggregation

r.scripts/sc-TissueMapper_RUN.D.R → r.scripts/sc-TissueMapper_RUN.D2.R

@@ -100,12 +100,12 @@ option_list=list(
   make_option("--st",action="store",default="TRUE",type='logical',help="Remove stressed cells?"),
   make_option("--stg",action="store",default="dws",type='character',help="Geneset to use for stress ID"),
   #make_option("--hpc.poststress",action="store",default=0.85,type='numeric',help="Max variance cutoff for PCs to use, post-stress"),
-  make_option("--res.poststress",action="store",default=0.2,type='numeric',help="Resolution to cluster, post-stress"),
-  make_option("--ds",action="store",default=0,type='integer',help="Number of cells to downsample"),
+  make_option("--res.poststress",action="store",default=0.1,type='numeric',help="Resolution to cluster, post-stress"),
+  make_option("--ds",action="store",default=50000,type='integer',help="Number of cells to downsample"),
   #make_option("--hpc.epi",action="store",default=0.85,type='numeric',help="Max variance cutoff for PCs to use, Epi"),
-  make_option("--res.epi",action="store",default=0.2,type='numeric',help="Resolution to cluster, Epi"),
+  make_option("--res.epi",action="store",default=0.1,type='numeric',help="Resolution to cluster, Epi"),
   #make_option("--hpc.st",action="store",default=0.85,type='numeric',help="Max variance cutoff for PCs to use, St"),
-  make_option("--res.st",action="store",default=0.2,type='numeric',help="Resolution to cluster, St")
+  make_option("--res.st",action="store",default=0.1,type='numeric',help="Resolution to cluster, St")
   )
 opt=parse_args(OptionParser(option_list=option_list))
 rm(option_list)
@@ -154,13 +154,14 @@ rm(results)
 rm(sc10x.D17)
 rm(sc10x.D27)
 
-results <- scPC(sc10x,lx=opt$lx,hx=opt$hx,ly=opt$ly,cc=opt$cc,pc=opt$pc,hpc=opt$hpc,file="pre.stress",cca=TRUE)
-sc10x <- results[[1]]
-genes.hvg <- results[[2]]
-pc.use <- results[[3]]
-rm(results)
+#results <- scPC(sc10x,lx=opt$lx,hx=opt$hx,ly=opt$ly,cc=opt$cc,pc=opt$pc,hpc=opt$hpc,file="pre.stress",cca=TRUE)
+#sc10x <- results[[1]]
+#genes.hvg <- results[[2]]
+#pc.use <- results[[3]]
+#rm(results)
 
-sc10x <- scCluster(sc10x,pc.use=pc.use,res.use=opt$res.prestress,folder="pre.stress")
+pc.use <- 25
+sc10x <- scCluster(sc10x,pc.use=pc.use,res.use=opt$res.prestress,folder="pre.stress",red="cca.aligned")
 
 if (opt$st==TRUE){
   results <- scStress(sc10x,stg=opt$stg,res.use=opt$res.prestress,pc.use=pc.use)
@@ -169,13 +170,13 @@ if (opt$st==TRUE){
   sc10x.Stress <- results[[3]]
   rm(results)
 
-  results <- scPC(sc10x,lx=opt$lx,hx=opt$hx,ly=opt$ly,cc=opt$cc,pc=opt$pc,hpc=opt$hpc,file="post.stress")
-  sc10x <- results[[1]]
-  genes.hvg.poststress <- results[[2]]
-  pc.use.poststress <- results[[3]]
-  rm(results)
+  #results <- scPC(sc10x,lx=opt$lx,hx=opt$hx,ly=opt$ly,cc=opt$cc,pc=opt$pc,hpc=opt$hpc,file="post.stress")
+  #sc10x <- results[[1]]
+  #genes.hvg.poststress <- results[[2]]
+  #pc.use.poststress <- results[[3]]
+  #rm(results)
   
-  sc10x <- scCluster(sc10x,pc.use=pc.use.poststress,res.use=opt$res.poststress,folder="post.stress")
+  sc10x <- scCluster(sc10x,pc.use=pc.use,res.use=opt$res.poststress,folder="post.stress",red="cca.aligned")
 }
 
 gene.set1 <- read_delim("./genesets/genes.deg.St.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
@@ -203,7 +204,7 @@ if (any(levels(sc10x@ident)=="Unknown")){
   sc10x.St <- scSubset(sc10x,i="Lin",g="St")
 }
 
-sc10x.Epi <- scCluster(sc10x.Epi,pc.use=pc.use.poststress,res.use=opt$res.epi,folder="epi")
+sc10x.Epi <- scCluster(sc10x.Epi,pc.use=pc.use,res.use=opt$res.epi,folder="epi",red="cca.aligned")
 
 gene.set1 <- read_delim("./genesets/genes.deg.BE.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
 gene.set1 <- gene.set1[1]
@@ -252,7 +253,7 @@ rm(gene.set1)
 results.cor.Epi.lgea <- scQuSAGEsm(sc10x.Epi,gs=gene.set,ds=opt$ds,nm="Epi.dws.sc",folder="lgea")
 rm(gene.set)
 
-sc10x.St <- scCluster(sc10x.St,pc.use=pc.use.poststress,res.use=opt$res.epi,folder="st")
+sc10x.St <- scCluster(sc10x.St,pc.use=pc.use,res.use=opt$res.epi,folder="st",red="cca.aligned")
 
 gene.set1 <- read_delim("./genesets/genes.deg.Fib.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
 gene.set1 <- gene.set1[1]