Add leu geneset, add 3grp cust col tSNE and finalize PdPbPc leu ID code

r.scripts/sc-TissueMapper.R

@@ -1257,6 +1257,51 @@ sctSNECustCol <- function(sc10x,i,bl,rd,file){
   dev.off()
 }
 
+sctSNE3CustCol <- function(sc10x,i,bl,rd,gn,file){
+  if (gn!=""){
+    gn.col <- NULL
+    if (length(sc10x@ident[sc10x@ident=="B-cells"])>0){
+      gn.col <- c(gn.col,"#BAE4B3")
+    }
+    if (length(sc10x@ident[sc10x@ident=="T-cells"])>0){
+      gn.col <- c(gn.col,"#74C476")
+    }
+    if (length(sc10x@ident[sc10x@ident=="Macrophages"])>0){
+      gn.col <- c(gn.col,"#31A354")
+    }
+    if (length(sc10x@ident[sc10x@ident=="Mast cells"])>0){
+      gn.col <- c(gn.col,"#006D2C")
+    }
+  }
+  sc10x <- SetAllIdent(object=sc10x,id=i)
+  if (any(sc10x@ident == "Unknown")){
+    sc10x@ident <- factor(sc10x@ident,levels=c(bl,rd,gn))
+  } else {
+    sc10x@ident <- factor(sc10x@ident,levels=c(bl,rd,gn,"Unknown"))
+  }
+  postscript(paste0("./analysis/diy/tSNE_",file,".",i,".CustCol.eps"))
+  if (length(bl)==1 & length(rd)==1 & length(gn)==1){
+    if (length(sc10x@ident[sc10x@ident=="Unknown"])>0){
+      plot <- TSNEPlot(object=sc10x,pt.size=2.5,do.label=FALSE,label.size=10,do.return=TRUE,vector.friendly=FALSE,colors.use=c(brewer.pal(5,"Blues")[5],brewer.pal(5,"Reds")[5],brewer.pal(5,"Greens")[5],"grey50"))
+    } else {
+      plot <- TSNEPlot(object=sc10x,pt.size=2.5,do.label=FALSE,label.size=10,do.return=TRUE,vector.friendly=FALSE,colors.use=c(brewer.pal(5,"Blues")[5],brewer.pal(5,"Reds")[5],brewer.pal(5,"Greens")[5]))
+    }
+    plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+    plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+    plot(plot)
+  } else {
+    if (bl!=""){
+      plot <- TSNEPlot(object=sc10x,pt.size=2.5,do.label=FALSE,label.size=10,do.return=TRUE,vector.friendly=FALSE,colors.use=c(brewer.pal((length(bl)+1),"Blues")[2:(length(bl)+1)],brewer.pal((length(rd)+1),"Reds")[2:(length(rd)+1)],gn.col))
+    } else {
+      plot <- TSNEPlot(object=sc10x,pt.size=2.5,do.label=FALSE,label.size=10,do.return=TRUE,vector.friendly=FALSE,colors.use=brewer.pal((length(rd)+1),"Reds")[2:(length(rd)+1)])
+    }
+    plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+    plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+    plot(plot)
+  }
+  dev.off()
+}
+
 sctSNEbwCol <- function(sc10x,i,file,files){
   sc10x <- SetAllIdent(object=sc10x,id=i)
   sc10x@ident <- factor(sc10x@ident,levels=c("Epi","BE","LE","OE1","OE_SCGB","OE2","OE_KRT13","St","Fib","SM","Endo","Leu","Unknown",1:50))

r.scripts/sc-TissueMapper_RUN.PdPbPc.R

+gc()
+library(methods)
+library(optparse)
+library(Seurat)
+library(readr)
+library(fBasics)
+library(pastecs)
+library(qusage)
+library(RColorBrewer)
+library(monocle)
+library(dplyr)
+library(viridis)
+library(readxl)
+
+source("../r.scripts/sc-TissueMapper.R")
+
+#Create folder structure
+setwd("../")
+if (!dir.exists("./analysis")){
+  dir.create("./analysis")
+}
+if (!dir.exists("./analysis/qc")){
+  dir.create("./analysis/qc")
+}
+if (!dir.exists("./analysis/qc/cc")){
+  dir.create("./analysis/qc/cc")
+}
+if (!dir.exists("./analysis/tSNE")){
+  dir.create("./analysis/tSNE")
+}
+if (!dir.exists("./analysis/tSNE/pre.stress")){
+  dir.create("./analysis/tSNE/pre.stress")
+}
+if (!dir.exists("./analysis/pca")){
+  dir.create("./analysis/pca")
+}
+if (!dir.exists("./analysis/pca/stress")){
+  dir.create("./analysis/pca/stress")
+}
+if (!dir.exists("./analysis/violin")){
+  dir.create("./analysis/violin")
+}
+if (!dir.exists("./analysis/violin/stress")){
+  dir.create("./analysis/violin/stress")
+}
+if (!dir.exists("./analysis/table")){
+  dir.create("./analysis/table")
+}
+if (!dir.exists("./analysis/tSNE/post.stress")){
+  dir.create("./analysis/tSNE/post.stress")
+}
+if (!dir.exists("./analysis/cor")){
+  dir.create("./analysis/cor")
+}
+if (!dir.exists("./analysis/tSNE/lin")){
+  dir.create("./analysis/tSNE/lin")
+}
+if (!dir.exists("./analysis/tSNE/epi")){
+  dir.create("./analysis/tSNE/epi")
+}
+if (!dir.exists("./analysis/tSNE/st")){
+  dir.create("./analysis/tSNE/st")
+}
+if (!dir.exists("./analysis/tSNE/leu")){
+  dir.create("./analysis/tSNE/leu")
+}
+if (!dir.exists("./analysis/tSNE/merge")){
+  dir.create("./analysis/tSNE/merge")
+}
+if (!dir.exists("./analysis/pca/ne")){
+  dir.create("./analysis/pca/ne")
+}
+if (!dir.exists("./analysis/tSNE/ne")){
+  dir.create("./analysis/tSNE/ne")
+}
+if (!dir.exists("./analysis/violin/ne")){
+  dir.create("./analysis/violin/ne")
+}
+if (!dir.exists("./analysis/tSNE/FINAL")){
+  dir.create("./analysis/tSNE/FINAL")
+}
+if (!dir.exists("./analysis/deg")){
+  dir.create("./analysis/deg")
+}
+if (!dir.exists("./analysis/cca")){
+  dir.create("./analysis/cca")
+}
+if (!dir.exists("./analysis/diy")){
+  dir.create("./analysis/diy")
+}
+if (!dir.exists("./analysis/pseudotime")){
+  dir.create("./analysis/pseudotime")
+}
+
+#Retrieve command-line options
+option_list=list(
+  make_option("--p",action="store",default="DPrF",type='character',help="Project Name"),
+  make_option("--g",action="store",default="ALL",type='character',help="Group To analyze"),
+  make_option("--lg",action="store",default=500,type='integer',help="Threshold for cells with minimum genes"),
+  make_option("--hg",action="store",default=3000,type='integer',help="Threshold for cells with maximum genes"),
+  make_option("--lm",action="store",default=0,type='numeric',help="Threshold for cells with minimum %mito genes"),
+  make_option("--hm",action="store",default=0.1,type='numeric',help="Threshold for cells with maximum %mito genes"),
+  make_option("--lx",action="store",default=0.2,type='numeric',help="x low threshold for hvg selection"),
+  make_option("--hx",action="store",default=5,type='numeric',help="x high threshold for hvg selection"),
+  make_option("--ly",action="store",default=1,type='numeric',help="y low threshold for hvg selection"),
+  make_option("--cc",action="store",default=TRUE,type='logical',help="Scale cell cycle?"),
+  make_option("--cca",action="store",default=50,type='integer',help="Number of CCAs to cacluate"),
+  make_option("--acca",action="store",default=30,type='integer',help="Number of CCAs to align"),
+  make_option("--pc",action="store",default=50,type='integer',help="Number of PCs to cacluate"),
+  make_option("--res.prestress",action="store",default=1,type='numeric',help="Resolution to cluster, pre-stress"),
+  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("--cut.stress",action="store",default=0.9,type='numeric',help="Cutoff for stress score"),
+  make_option("--res.poststress",action="store",default=0.5,type='numeric',help="Resolution to cluster, post-stress"),
+  make_option("--cut.ne",action="store",default=0.999,type='numeric',help="Cutoff for NE score")
+)
+opt=parse_args(OptionParser(option_list=option_list))
+rm(option_list)
+if (opt$lm==0){opt$lm=-Inf}
+
+load("./analysis/sc10x.Rda")
+
+for (i in c("Pb","Pc","Pd")){
+  if (!dir.exists(paste0("./analysis/tSNE/post.stress/",i))){
+    dir.create(paste0("./analysis/tSNE/post.stress/",i))
+  }
+    if (!dir.exists(paste0("./analysis/tSNE/lin/",i))){
+    dir.create(paste0("./analysis/tSNE/lin/",i))
+  }
+  if (!dir.exists(paste0("./analysis/tSNE/epi/",i))){
+    dir.create(paste0("./analysis/tSNE/epi/",i))
+  }
+  if (!dir.exists(paste0("./analysis/tSNE/st/",i))){
+    dir.create(paste0("./analysis/tSNE/st/",i))
+  }
+  if (!dir.exists(paste0("./analysis/tSNE/leu/",i))){
+    dir.create(paste0("./analysis/tSNE/leu/",i))
+  }
+
+  sc10x <- get(paste0("sc10x.",i))
+    
+  sc10x <- scCluster(sc10x,pc.use=opt$acca,res.use=0.5,folder=paste0("post.stress/",i),red="cca.aligned")
+  
+  gene.set1 <- read_delim("./genesets/DEG_Epi_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "Epi"
+  gene.set <- c(gene.set1)
+  gene.set1 <- read_delim("./genesets/DEG_FMSt_5FC.txt","\t",escape_double=FALSE,trim_ws=TRUE,col_names=FALSE)
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "St"
+  gene.set <- c(gene.set,gene.set1)
+  rm(gene.set1)
+  gc()
+  min.all <- min(table(sc10x@meta.data[,paste0("res",0.5)]))
+  results <- scQuSAGE(sc10x,gs=gene.set,res.use=opt$res.poststress,ds=min.all,nm="Lin",folder=paste0("lin/",i))
+  sc10x <- results[[1]]
+  results.cor.Lin <- results[[2]]
+  results.clust.Lin.id <- results[[3]]
+  rm(results)
+  rm(gene.set)
+  
+  sc10x <- SetAllIdent(object=sc10x,id="Lin")
+  sc10x.Epi <- scSubset(sc10x,i="Lin",g="Epi")
+  if (any(levels(sc10x@ident)=="Unknown")){
+    sc10x.St <- scSubset(sc10x,i="Lin",g=c("St","Unknown"))
+  } else {
+    sc10x.St <- scSubset(sc10x,i="Lin",g="St")
+  }
+  
+  sc10x.Epi <- scCluster(sc10x.Epi,pc.use=opt$acca,res.use=opt$res.poststress,folder=paste0("epi/",i),red="cca.aligned")
+  
+  #sc10x.Epi <- SetAllIdent(object=sc10x.Epi,id=paste0("res",opt$res.poststress))
+  #sc10x.Epi <- BuildClusterTree(sc10x.Epi,do.reorder=TRUE,reorder.numeric=TRUE,do.plot=FALSE)
+  sc10x.Epi <- StashIdent(object=sc10x.Epi,save.name=paste0("res",opt$res.poststress))
+  
+  sc10x.Epi <- RunTSNE(object=sc10x.Epi,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE)
+  postscript(paste0("./analysis/tSNE/epi/",i,"/tSNE_Sample.eps"))
+  plot <- TSNEPlot(object=sc10x.Epi,group.by="samples",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE)
+  plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+  plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot(plot)
+  dev.off()
+  postscript(paste0("./analysis/tSNE/epi/",i,"/tSNE_res",opt$res.poststress,".eps"))
+  plot <- TSNEPlot(object=sc10x.Epi,pt.size=5,do.label=TRUE,label.size=10,do.return=TRUE,vector.friendly=FALSE)
+  plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+  plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot(plot)
+  dev.off()
+  rm(plot)
+  
+  gene.set1 <- read_delim("./genesets/genes.deg.BE.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+  gene.set1 <- gene.set1[1]
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "BE"
+  gene.set <- c(gene.set1)
+  gene.set1 <- read_delim("./genesets/genes.deg.LE.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+  gene.set1 <- gene.set1[1]
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "LE"
+  gene.set <- c(gene.set,gene.set1)
+  gene.set1 <- read_delim("./genesets/genes.deg.OE1.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+  gene.set1 <- gene.set1[1]
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "Club"
+  gene.set <- c(gene.set,gene.set1)
+  gene.set1 <- read_delim("./genesets/genes.deg.OE2.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+  gene.set1 <- gene.set1[1]
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "Hillock"
+  gene.set <- c(gene.set,gene.set1)
+  rm(gene.set1)
+  gc()
+  min.epi <- min(table(sc10x.Epi@meta.data[,paste0("res",opt$res.poststress)]))
+  results <- scQuSAGE(sc10x.Epi,gs=gene.set,res.use=opt$res.poststress,ds=min.epi,nm="Epi.dws.sc",folder=paste0("epi/",i))
+  sc10x.Epi <- results[[1]]
+  results.cor.Epi <- results[[2]]
+  results.clust.Epi.id <- results[[3]]
+  rm(results)
+  rm(gene.set)
+  
+  
+  sc10x.St <- scCluster(sc10x.St,pc.use=opt$acca,res.use=opt$res.poststress,folder=paste0("st/",i),red="cca.aligned")
+  
+  #sc10x.St <- SetAllIdent(object=sc10x.St,id=paste0("res",opt$res.poststress))
+  #sc10x.St <- BuildClusterTree(sc10x.St,do.reorder=TRUE,reorder.numeric=TRUE,do.plot=FALSE)
+  sc10x.St <- StashIdent(object=sc10x.St,save.name=paste0("res",opt$res.poststress))
+  
+  sc10x.St <- RunTSNE(object=sc10x.St,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE)
+  postscript(paste0("./analysis/tSNE/st/",i,"/tSNE_Sample.eps"))
+  plot <- TSNEPlot(object=sc10x.St,group.by="samples",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE)
+  plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+  plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot(plot)
+  dev.off()
+  postscript(paste0("./analysis/tSNE/st/",i,"/tSNE_res",opt$res.poststress,".eps"))
+  plot <- TSNEPlot(object=sc10x.St,pt.size=5,do.label=TRUE,label.size=10,do.return=TRUE,vector.friendly=FALSE)
+  plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+  plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot(plot)
+  dev.off()
+  rm(plot)
+  
+  gene.set1 <- read_delim("./genesets/genes.deg.Endo.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+  gene.set1 <- gene.set1[1]
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "Endo"
+  gene.set <- c(gene.set1)
+  gene.set1 <- read_delim("./genesets/genes.deg.SM.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+  gene.set1 <- gene.set1[1]
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "SM"
+  gene.set <- c(gene.set,gene.set1)
+  gene.set1 <- read_delim("./genesets/genes.deg.Fib.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
+  gene.set1 <- gene.set1[1]
+  gene.set1 <- as.list(gene.set1)
+  names(gene.set1) <- "Fib"
+  gene.set <- c(gene.set,gene.set1)
+  
+  genes.leu <- read_excel("genesets/40425_2017_215_MOESM1_ESM.xlsx",sheet="S3. Candidate markers")
+  leu <- as.list(unique(genes.leu[,2]))$Cell
+  leu <- leu[-c(1,3,4,7:9,14,15,17:18,20:21,23:30)]
+  genes.leu <- genes.leu[genes.leu$Selected==1,]
+  genes.leu <- genes.leu[unlist(genes.leu[,2]) %in% unlist(leu),]
+  gene.set1 <- split(genes.leu[,1], genes.leu[,2])
+  gene.set1 <- lapply(gene.set1,unname)
+  gene.set1 <- lapply(gene.set1,unlist)
+  gene.set <- c(gene.set,gene.set1)
+  
+  rm(gene.set1)
+  gc()
+  min.st <- min(table(sc10x.St@meta.data[,paste0("res",opt$res.poststress)]))
+  results <- scQuSAGE(sc10x.St,gs=gene.set,res.use=opt$res.poststress,ds=min.st,nm="St.dws.sc",folder=paste0("st/",i))
+  sc10x.St <- results[[1]]
+  results.cor.St <- results[[2]]
+  results.clust.St.id <- results[[3]]
+  rm(results)
+  rm(gene.set)
+  
+  sc10x.St <- SetAllIdent(object=sc10x.St,id="St.dws.sc")
+  sc10x.Leu <- scSubset(sc10x.St,i="St.dws.sc",g=leu[leu %in% unique(sc10x.St@ident)])
+  
+  sc10x.Leu <- RunTSNE(object=sc10x.Leu,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE)
+  postscript(paste0("./analysis/tSNE/leu/",i,"/tSNE_Sample.eps"))
+  plot <- TSNEPlot(object=sc10x.Leu,group.by="samples",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE)
+  plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+  plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot(plot)
+  dev.off()
+  postscript(paste0("./analysis/tSNE/leu/",i,"/tSNE_LeuLin.eps"))
+  plot <- TSNEPlot(object=sc10x.Leu,pt.size=5,do.label=TRUE,label.size=10,do.return=TRUE,vector.friendly=FALSE)
+  plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20))
+  plot <- plot+guides(colour=guide_legend(override.aes=list(size=10)))
+  plot(plot)
+  dev.off()
+  rm(plot)
+  
+  sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Epi.dws.sc",i.2="St.dws.sc",nm="Merge")
+  
+  sc10x@ident <- factor(sc10x@ident,levels=c("BE","LE","Hillock","Club","Fib","SM","Endo","B-cells","T-cells","Macrophages","Mast cells"))
+  sctSNE3CustCol(sc10x,i="Merge",bl=c("BE","LE","Hillock","Club"),rd=c("Fib","SM","Endo"),gn=c("B-cells","T-cells","Macrophages","Mast cells"),file=i)
+
+  if (i=="Pd"){
+    for (j in list(c("D17PrPzF_Via","D27PrPzF_Via","D35PrPzF_Via"),c("D17PrTzF_Via","D27PrTzF_Via","D35PrTzF_Via"))){
+      sc10x.sub <- scSubset(sc10x,"samples",j)
+      sc10x.sub <- SetAllIdent(object=sc10x.sub,id="Merge")
+      sc10x.sub@ident <- factor(sc10x.sub@ident,levels=c("BE","LE","Hillock","Club","Fib","SM","Endo","B-cells","T-cells","Macrophages","Mast cells"))
+      sctSNE3CustCol(sc10x.sub,i="Merge",bl=c("BE","LE","Hillock","Club"),rd=c("Fib","SM","Endo"),gn=c("B-cells","T-cells","Macrophages","Mast cells"),file=paste0(i,".",substr(j[1],6,7)))
+    }
+  rm(sc10x.sub)
+  }
+
+  write.table(table(sc10x@meta.data[,"samples"],sc10x@meta.data[,"Merge"]),file=paste0("./analysis/table/Table_samples_Merge",i,".csv"),row.names=TRUE,col.names=NA,append=FALSE,sep=",")
+  write.table(round(prop.table(table(sc10x@meta.data[,"samples"],sc10x@meta.data[,"Merge"]),1)*100,1),file=paste0("./analysis/table/ProbTable_samples_Merge",i,".csv"),row.names=TRUE,col.names=NA,append=FALSE,sep=",")
+  
+  assign(paste0("sc10x.",i,".analized"),sc10x)
+  assign(paste0("sc10x.Epi.",i,".analized"),sc10x.Epi)
+  assign(paste0("sc10x.St.",i,".analized"),sc10x.St)
+  assign(paste0("sc10x.Leu.",i,".analized"),sc10x.Leu)
+  assign(paste0("results.cor.Lin.",i),results.cor.Lin)
+  assign(paste0("results.clust.Lin.id.",i),results.clust.Lin.id)
+  assign(paste0("results.cor.Epi.",i),results.cor.Epi)
+  assign(paste0("results.clust.Epi.id.",i),results.clust.Epi.id)
+  assign(paste0("results.cor.St.",i),results.cor.St)
+  assign(paste0("results.clust.St.id.",i),results.clust.St.id)
+  rm(sc10x.sub)  
+  rm(sc10x)
+  rm(sc10x.Epi)
+  rm(sc10x.St)
+  rm(sc10x.Leu)
+  rm(results.cor.Lin)
+  rm(results.cor.Epi)
+  rm(results.cor.St)
+  rm(results.clust.Lin.id)
+  rm(results.clust.Epi.id)
+  rm(results.clust.St.id)
+  rm(min.all)
+  rm(min.epi)
+  rm(min.st)
+  rm(i)
+}
+save.image(file="./analysis/sc10x.analized.RData")
+