From 84c4e944ba34806811fcdef31d9378bc7bcb51bc Mon Sep 17 00:00:00 2001 From: "Gervaise H. Henry" <gervaise.henry@utsouthwestern.edu> Date: Fri, 26 Oct 2018 09:40:21 -0500 Subject: [PATCH] Finalize (?) all code for PdPbPc to run --- bash.scripts/sc_TissueMapper-PdPbPc.sh | 17 ++ r.scripts/sc-TissueMapper_RUN.PdPbPc.Pb.R | 194 ++-------------------- r.scripts/sc-TissueMapper_RUN.PdPbPc.Pc.R | 178 +------------------- r.scripts/sc-TissueMapper_RUN.PdPbPc.Pd.R | 194 ++-------------------- r.scripts/sc-TissueMapper_RUN.PdPbPc.R | 4 +- 5 files changed, 43 insertions(+), 544 deletions(-) create mode 100644 bash.scripts/sc_TissueMapper-PdPbPc.sh diff --git a/bash.scripts/sc_TissueMapper-PdPbPc.sh b/bash.scripts/sc_TissueMapper-PdPbPc.sh new file mode 100644 index 0000000..7422a5c --- /dev/null +++ b/bash.scripts/sc_TissueMapper-PdPbPc.sh @@ -0,0 +1,17 @@ +#!/bin/bash +#SBATCH --job-name R_FullAnalysis +#SBATCH -p 256GB,256GBv1,384GB +#SBATCH -N 1 +#SBATCH -t 7-0:0:0 +#SBATCH -o job_%j.out +#SBATCH -e job_%j.out +#SBATCH --mail-type ALL +#SBATCH --mail-user gervaise.henry@utsouthwestern.edu + +module load R/3.4.1-gccmkl + +Rscript ../r.scripts/sc-TissueMapper_RUN.PdPbPc.Pd.R & +Rscript ../r.scripts/sc-TissueMapper_RUN.PdPbPc.Pb.R & +Rscript ../r.scripts/sc-TissueMapper_RUN.PdPbPc.Pc.R & +wait +Rscript ../r.scripts/sc-TissueMapper_RUN.PdPbPc.R diff --git a/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pb.R b/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pb.R index e42948f..4a79f53 100644 --- a/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pb.R +++ b/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pb.R @@ -114,7 +114,7 @@ opt=parse_args(OptionParser(option_list=option_list)) rm(option_list) if (opt$lm==0){opt$lm=-Inf} -sc10x <- scLoad("Pb") +sc10x <- scLoad("Pb",sub="Pb) sc10x <- scSubset(sc10x,i="Glandular",g="Glandular") @@ -145,20 +145,18 @@ if (opt$cc==TRUE){ } gc() -sc10x.C11 <- scSubset(sc10x,"samples","VAMC011PrBlF_Via") -sc10x.C12 <- scSubset(sc10x,"samples","VAMC012PrRdF_Via") -sc10x.C13 <- scSubset(sc10x,"samples","VAMC013PrRdF_Via") -sc10x.C14 <- scSubset(sc10x,"samples","VAMC014PrRdF_Via") +sc10x.B327 <- scSubset(sc10x,"samples","BPH327PrGF_Via") +sc10x.B340 <- scSubset(sc10x,"samples","BPH340PrSF_Via") +sc10x.B342 <- scSubset(sc10x,"samples","BPH342PrF_Via") -results <- sc4CCA(sc10x.C11,sc10x.C12,sc10x.C13,sc10x.C14,"C11","C12","C13","C14",cc=opt$cc,cca=opt$cca,acca=opt$acca,lx=opt$lx,hx=opt$hx,ly=opt$ly) +results <- sc3CCA(sc10x.B327,sc10x.B340,sc10x.B342,"B327","B340","B342",cc=opt$cc,cca=opt$cca,acca=opt$acca,lx=opt$lx,hx=opt$hx,ly=opt$ly) sc10x <- results[[1]] genes.hvg.cca <- results[[2]] rm(results) -rm(sc10x.C11) -rm(sc10x.C12) -rm(sc10x.C13) -rm(sc10x.C14) +rm(sc10x.B327) +rm(sc10x.B340) +rm(sc10x.B342) sc10x <- scCluster(sc10x,pc.use=opt$acca,res.use=opt$res.prestress,folder="pre.stress",red="cca.aligned") @@ -168,179 +166,7 @@ if (opt$st==TRUE){ counts.cell.filtered.stress <- results[[2]] sc10x.Stress <- results[[3]] rm(results) - - sc10x <- scCluster(sc10x,pc.use=opt$acca,res.use=opt$res.poststress,folder="post.stress",red="cca.aligned") } -gene.set1 <- read_delim("./genesets/genes.deg.Epi.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Epi" -gene.set <- c(gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.St.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -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",opt$res.poststress)])) -results <- scQuSAGE(sc10x,gs=gene.set,res.use=opt$res.poststress,ds=min.all,nm="Lin",folder="lin") -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 <- 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.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.Epi <- RunTSNE(object=sc10x.Epi,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE) -postscript(paste0("./analysis/tSNE/epi/tSNE_Sample.eps")) -plot <- TSNEPlot(object=sc10x.Epi,group.by="samples",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE) -plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20)) -plot <- plot+guides(colour=guide_legend(override.aes=list(size=10))) -plot(plot) -dev.off() -postscript(paste0("./analysis/tSNE/epi/tSNE_res",opt$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) <- "OE_SCGB" -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) <- "OE_KRT13" -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="epi") -sc10x.Epi <- results[[1]] -results.cor.Epi.dws <- results[[2]] -results.clust.Epi.dws.id <- results[[3]] -rm(results) -rm(gene.set) - -sc10x.St <- RunTSNE(object=sc10x.St,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE) -postscript(paste0("./analysis/tSNE/st/tSNE_Sample.eps")) -plot <- TSNEPlot(object=sc10x.St,group.by="samples",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE) -plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20)) -plot <- plot+guides(colour=guide_legend(override.aes=list(size=10))) -plot(plot) -dev.off() -postscript(paste0("./analysis/tSNE/st/tSNE_res",opt$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) -gene.set1 <- read_delim("./genesets/genes.deg.Leu.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Leu" -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="st") -sc10x.St <- results[[1]] -results.cor.St.go <- results[[2]] -results.clust.St.go.id <- results[[3]] -rm(results) -rm(gene.set) - -sc10x.Epi.NE <- scNE(sc10x.Epi,neg="dws",cut=opt$cut.ne) - -sc10x.Epi <- scMergeSubClust(sc10x.Epi,i="Epi.dws.sub",g=c("BE","LE"),nm="Merge") - -sc10x.St <- scMergeSubClust(sc10x.St,i="St.go",g=c("Endo","SM","Fib","Leu"),nm="Merge") - -sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Merge",i.2="Merge",nm="Merge_Epi.dws_St.go") - -sc10x <- scMerge(sc10x,sc10x,sc10x.Epi.NE,i.1="Merge_Epi.dws_St.go",i.2="NE",nm="Merge_Epi.dws_St.go_NE") - -sc10x.Epi <- scMerge(sc10x.Epi,sc10x.Epi,sc10x.Epi.NE,i.1="Epi.dws.sub",i.2="NE",nm="Epi.dws.sub_NE") - -sc10x <- SetAllIdent(object=sc10x,id="Lin") -sc10x <- SetIdent(object=sc10x,cells.use=names(sc10x@ident[sc10x@ident %in% c("St","Unknown")]),ident.use="St") -sc10x <- StashIdent(object=sc10x,save.name="mLin") - -sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Epi.dws.sc",i.2="St.dws.sc",nm="Merge_Epi.dws.sc_St.dws.sc") - -sc10x <- scMerge(sc10x,sc10x,sc10x.Epi.NE,i.1="Merge_Epi.dws.sc_St.dws.sc",i.2="NE",nm="Merge_Epi.dws.sc_St.dws.sc_NE") - -sc10x.Epi <- scMerge(sc10x.Epi,sc10x.Epi,sc10x.Epi.NE,i.1="Epi.dws.sc",i.2="NE",nm="Epi.dws.sc_NE") - -sc10x <- SetAllIdent(object=sc10x,id="Merge_Epi.dws.sc_St.dws.sc") -sc10x@ident <- factor(sc10x@ident,levels=c("BE","LE","OE_SCGB","OE_KRT13","Fib","SM","Endo","Leu","Unknown")) -postscript("./analysis/tSNE/FINAL/tSNE_FINAL.eps") -plot <- TSNEPlot(object=sc10x,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() - -scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc") -scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc_NE") -scTables(sc10x,i.1="Merge_Epi.dws.sc_St.dws.sc_NE",i.2="Merge_Epi.dws.sc_St.dws.sc") - -sctSNECustCol(sc10x,i="Merge_Epi.dws.sc_St.dws.sc",bl=c("BE","LE","OE_SCGB","OE_KRT13"),rd=c("Fib","SM","Endo","Leu","Unknown"),file="D") - -save(list=ls(pattern="sc10x.Stress"),file="./analysis/sc10x.Stress.Rda") -rm(list=ls(pattern="sc10x.Stress")) -save(list=ls(pattern="^genes.deg"),file="./analysis/DEG.Rda") -rm(list=ls(pattern="^genes.deg")) -save(list=ls(pattern="sc10x.Epi"),file="./analysis/sc10x.Epi.Rda") -rm(list=ls(pattern="^sc10x.Epi")) -save(list=ls(pattern="sc10x.St"),file="./analysis/sc10x.St.Rda") -rm(list=ls(pattern="sc10x.St")) -save(list=ls(pattern="^sc10x"),file="./analysis/sc10x.Rda") -rm(list=ls(pattern="^sc10x")) -save.image(file="./analysis/Data.RData") +sc10x.Pb <- sc10x +save(sc10x.Pb,file="./analysis/sc10x.Pb.Rda") diff --git a/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pc.R b/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pc.R index f277894..563db10 100644 --- a/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pc.R +++ b/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pc.R @@ -114,7 +114,7 @@ opt=parse_args(OptionParser(option_list=option_list)) rm(option_list) if (opt$lm==0){opt$lm=-Inf} -sc10x <- scLoad("Pc") +sc10x <- scLoad("Pc",sub="Pc") sc10x <- scSubset(sc10x,i="Tumor",g="Tumor") @@ -168,179 +168,7 @@ if (opt$st==TRUE){ counts.cell.filtered.stress <- results[[2]] sc10x.Stress <- results[[3]] rm(results) - - sc10x <- scCluster(sc10x,pc.use=opt$acca,res.use=opt$res.poststress,folder="post.stress",red="cca.aligned") } -gene.set1 <- read_delim("./genesets/genes.deg.Epi.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Epi" -gene.set <- c(gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.St.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -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",opt$res.poststress)])) -results <- scQuSAGE(sc10x,gs=gene.set,res.use=opt$res.poststress,ds=min.all,nm="Lin",folder="lin") -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 <- 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.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.Epi <- RunTSNE(object=sc10x.Epi,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE) -postscript(paste0("./analysis/tSNE/epi/tSNE_Sample.eps")) -plot <- TSNEPlot(object=sc10x.Epi,group.by="samples",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE) -plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20)) -plot <- plot+guides(colour=guide_legend(override.aes=list(size=10))) -plot(plot) -dev.off() -postscript(paste0("./analysis/tSNE/epi/tSNE_res",opt$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) <- "OE_SCGB" -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) <- "OE_KRT13" -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="epi") -sc10x.Epi <- results[[1]] -results.cor.Epi.dws <- results[[2]] -results.clust.Epi.dws.id <- results[[3]] -rm(results) -rm(gene.set) - -sc10x.St <- RunTSNE(object=sc10x.St,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE) -postscript(paste0("./analysis/tSNE/st/tSNE_Sample.eps")) -plot <- TSNEPlot(object=sc10x.St,group.by="samples",pt.size=2.5,do.return=TRUE,vector.friendly=FALSE) -plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20)) -plot <- plot+guides(colour=guide_legend(override.aes=list(size=10))) -plot(plot) -dev.off() -postscript(paste0("./analysis/tSNE/st/tSNE_res",opt$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) -gene.set1 <- read_delim("./genesets/genes.deg.Leu.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Leu" -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="st") -sc10x.St <- results[[1]] -results.cor.St.go <- results[[2]] -results.clust.St.go.id <- results[[3]] -rm(results) -rm(gene.set) - -sc10x.Epi.NE <- scNE(sc10x.Epi,neg="dws",cut=opt$cut.ne) - -sc10x.Epi <- scMergeSubClust(sc10x.Epi,i="Epi.dws.sub",g=c("BE","LE"),nm="Merge") - -sc10x.St <- scMergeSubClust(sc10x.St,i="St.go",g=c("Endo","SM","Fib","Leu"),nm="Merge") - -sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Merge",i.2="Merge",nm="Merge_Epi.dws_St.go") - -sc10x <- scMerge(sc10x,sc10x,sc10x.Epi.NE,i.1="Merge_Epi.dws_St.go",i.2="NE",nm="Merge_Epi.dws_St.go_NE") - -sc10x.Epi <- scMerge(sc10x.Epi,sc10x.Epi,sc10x.Epi.NE,i.1="Epi.dws.sub",i.2="NE",nm="Epi.dws.sub_NE") - -sc10x <- SetAllIdent(object=sc10x,id="Lin") -sc10x <- SetIdent(object=sc10x,cells.use=names(sc10x@ident[sc10x@ident %in% c("St","Unknown")]),ident.use="St") -sc10x <- StashIdent(object=sc10x,save.name="mLin") - -sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Epi.dws.sc",i.2="St.dws.sc",nm="Merge_Epi.dws.sc_St.dws.sc") - -sc10x <- scMerge(sc10x,sc10x,sc10x.Epi.NE,i.1="Merge_Epi.dws.sc_St.dws.sc",i.2="NE",nm="Merge_Epi.dws.sc_St.dws.sc_NE") - -sc10x.Epi <- scMerge(sc10x.Epi,sc10x.Epi,sc10x.Epi.NE,i.1="Epi.dws.sc",i.2="NE",nm="Epi.dws.sc_NE") - -sc10x <- SetAllIdent(object=sc10x,id="Merge_Epi.dws.sc_St.dws.sc") -sc10x@ident <- factor(sc10x@ident,levels=c("BE","LE","OE_SCGB","OE_KRT13","Fib","SM","Endo","Leu","Unknown")) -postscript("./analysis/tSNE/FINAL/tSNE_FINAL.eps") -plot <- TSNEPlot(object=sc10x,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() - -scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc") -scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc_NE") -scTables(sc10x,i.1="Merge_Epi.dws.sc_St.dws.sc_NE",i.2="Merge_Epi.dws.sc_St.dws.sc") - -sctSNECustCol(sc10x,i="Merge_Epi.dws.sc_St.dws.sc",bl=c("BE","LE","OE_SCGB","OE_KRT13"),rd=c("Fib","SM","Endo","Leu","Unknown"),file="D") - -save(list=ls(pattern="sc10x.Stress"),file="./analysis/sc10x.Stress.Rda") -rm(list=ls(pattern="sc10x.Stress")) -save(list=ls(pattern="^genes.deg"),file="./analysis/DEG.Rda") -rm(list=ls(pattern="^genes.deg")) -save(list=ls(pattern="sc10x.Epi"),file="./analysis/sc10x.Epi.Rda") -rm(list=ls(pattern="^sc10x.Epi")) -save(list=ls(pattern="sc10x.St"),file="./analysis/sc10x.St.Rda") -rm(list=ls(pattern="sc10x.St")) -save(list=ls(pattern="^sc10x"),file="./analysis/sc10x.Rda") -rm(list=ls(pattern="^sc10x")) -save.image(file="./analysis/Data.RData") +sc10x.Pc <- sc10x +save(sc10x,file="./analysis/sc10x.Pc.Rda") diff --git a/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pd.R b/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pd.R index dddb26a..0aef238 100644 --- a/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pd.R +++ b/r.scripts/sc-TissueMapper_RUN.PdPbPc.Pd.R @@ -114,7 +114,7 @@ opt=parse_args(OptionParser(option_list=option_list)) rm(option_list) if (opt$lm==0){opt$lm=-Inf} -sc10x <- scLoad("Pd") +sc10x <- scLoad("Pd",sub="Pd") sc10x <- scSubset(sc10x,i="ALL",g="ALL") @@ -145,20 +145,18 @@ if (opt$cc==TRUE){ } gc() -sc10x.C11 <- scSubset(sc10x,"samples","VAMC011PrBlF_Via") -sc10x.C12 <- scSubset(sc10x,"samples","VAMC012PrRdF_Via") -sc10x.C13 <- scSubset(sc10x,"samples","VAMC013PrRdF_Via") -sc10x.C14 <- scSubset(sc10x,"samples","VAMC014PrRdF_Via") +sc10x.D17 <- scSubset(sc10x,"patient","D17") +sc10x.D27 <- scSubset(sc10x,"patient","D27") +sc10x.D35 <- scSubset(sc10x,"patient","D35") -results <- sc4CCA(sc10x.C11,sc10x.C12,sc10x.C13,sc10x.C14,"C11","C12","C13","C14",cc=opt$cc,cca=opt$cca,acca=opt$acca,lx=opt$lx,hx=opt$hx,ly=opt$ly) +results <- sc3CCA(sc10x.D17,sc10x.D27,sc10x.D35,"D17","D27","D35",cc=opt$cc,cca=opt$cca,acca=opt$acca,lx=opt$lx,hx=opt$hx,ly=opt$ly) sc10x <- results[[1]] genes.hvg.cca <- results[[2]] rm(results) -rm(sc10x.C11) -rm(sc10x.C12) -rm(sc10x.C13) -rm(sc10x.C14) +rm(sc10x.D17) +rm(sc10x.D27) +rm(sc10x.D35) sc10x <- scCluster(sc10x,pc.use=opt$acca,res.use=opt$res.prestress,folder="pre.stress",red="cca.aligned") @@ -168,179 +166,7 @@ if (opt$st==TRUE){ counts.cell.filtered.stress <- results[[2]] sc10x.Stress <- results[[3]] rm(results) - - sc10x <- scCluster(sc10x,pc.use=opt$acca,res.use=opt$res.poststress,folder="post.stress",red="cca.aligned") } -gene.set1 <- read_delim("./genesets/genes.deg.Epi.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Epi" -gene.set <- c(gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.St.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "St" -gene.set <- c(gene.set,gene.set1) -rm(gene.set1) -gc() -min.all <- min(table(sc10x@meta.data[,paste0("res",opt$res.poststress)])) -results <- scQuSAGE(sc10x,gs=gene.set,res.use=opt$res.poststress,ds=min.all,nm="Lin",folder="lin") -sc10x <- results[[1]] -results.cor.Lin <- results[[2]] -results.clust.Lin.id <- results[[3]] -rm(results) -rm(gene.set) - -sc10x <- SetAllIdent(object=sc10x,id="Lin") -sc10x.Epi <- scSubset(sc10x,i="Lin",g="Epi") -if (any(levels(sc10x@ident)=="Unknown")){ - sc10x.St <- scSubset(sc10x,i="Lin",g=c("St","Unknown")) -} else { - sc10x.St <- scSubset(sc10x,i="Lin",g="St") -} -sc10x.Epi <- SetAllIdent(object=sc10x.Epi,id=paste0("res",opt$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$res.poststress)) -sc10x.St <- SetAllIdent(object=sc10x.St,id=paste0("res",opt$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$res.poststress)) - -sc10x.Epi <- RunTSNE(object=sc10x.Epi,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE) -postscript(paste0("./analysis/tSNE/epi/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/tSNE_res",opt$res.poststress,".eps")) -plot <- TSNEPlot(object=sc10x.Epi,pt.size=5,do.label=TRUE,label.size=10,do.return=TRUE,vector.friendly=FALSE) -plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20)) -plot <- plot+guides(colour=guide_legend(override.aes=list(size=10))) -plot(plot) -dev.off() -rm(plot) - -gene.set1 <- read_delim("./genesets/genes.deg.BE.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "BE" -gene.set <- c(gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.LE.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "LE" -gene.set <- c(gene.set,gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.OE1.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "OE_SCGB" -gene.set <- c(gene.set,gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.OE2.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "OE_KRT13" -gene.set <- c(gene.set,gene.set1) -rm(gene.set1) -gc() -min.epi <- min(table(sc10x.Epi@meta.data[,paste0("res",opt$res.poststress)])) -results <- scQuSAGE(sc10x.Epi,gs=gene.set,res.use=opt$res.poststress,ds=min.epi,nm="Epi.dws.sc",folder="epi") -sc10x.Epi <- results[[1]] -results.cor.Epi.dws <- results[[2]] -results.clust.Epi.dws.id <- results[[3]] -rm(results) -rm(gene.set) - -sc10x.St <- RunTSNE(object=sc10x.St,reduction.use="cca.aligned",dims.use=1:opt$acca,do.fast=TRUE) -postscript(paste0("./analysis/tSNE/st/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/tSNE_res",opt$res.poststress,".eps")) -plot <- TSNEPlot(object=sc10x.St,pt.size=5,do.label=TRUE,label.size=10,do.return=TRUE,vector.friendly=FALSE) -plot <- plot+theme(axis.text.x=element_text(size=20),axis.text.y=element_text(size=20),axis.title.x=element_text(size=20),axis.title.y=element_text(size=20),legend.text=element_text(size=20)) -plot <- plot+guides(colour=guide_legend(override.aes=list(size=10))) -plot(plot) -dev.off() -rm(plot) - -gene.set1 <- read_delim("./genesets/genes.deg.Endo.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Endo" -gene.set <- c(gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.SM.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "SM" -gene.set <- c(gene.set,gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.Fib.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Fib" -gene.set <- c(gene.set,gene.set1) -gene.set1 <- read_delim("./genesets/genes.deg.Leu.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE) -gene.set1 <- gene.set1[1] -gene.set1 <- as.list(gene.set1) -names(gene.set1) <- "Leu" -gene.set <- c(gene.set,gene.set1) -rm(gene.set1) -gc() -min.st <- min(table(sc10x.St@meta.data[,paste0("res",opt$res.poststress)])) -results <- scQuSAGE(sc10x.St,gs=gene.set,res.use=opt$res.poststress,ds=min.st,nm="St.dws.sc",folder="st") -sc10x.St <- results[[1]] -results.cor.St.go <- results[[2]] -results.clust.St.go.id <- results[[3]] -rm(results) -rm(gene.set) - -sc10x.Epi.NE <- scNE(sc10x.Epi,neg="dws",cut=opt$cut.ne) - -sc10x.Epi <- scMergeSubClust(sc10x.Epi,i="Epi.dws.sub",g=c("BE","LE"),nm="Merge") - -sc10x.St <- scMergeSubClust(sc10x.St,i="St.go",g=c("Endo","SM","Fib","Leu"),nm="Merge") - -sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Merge",i.2="Merge",nm="Merge_Epi.dws_St.go") - -sc10x <- scMerge(sc10x,sc10x,sc10x.Epi.NE,i.1="Merge_Epi.dws_St.go",i.2="NE",nm="Merge_Epi.dws_St.go_NE") - -sc10x.Epi <- scMerge(sc10x.Epi,sc10x.Epi,sc10x.Epi.NE,i.1="Epi.dws.sub",i.2="NE",nm="Epi.dws.sub_NE") - -sc10x <- SetAllIdent(object=sc10x,id="Lin") -sc10x <- SetIdent(object=sc10x,cells.use=names(sc10x@ident[sc10x@ident %in% c("St","Unknown")]),ident.use="St") -sc10x <- StashIdent(object=sc10x,save.name="mLin") - -sc10x <- scMerge(sc10x,sc10x.Epi,sc10x.St,i.1="Epi.dws.sc",i.2="St.dws.sc",nm="Merge_Epi.dws.sc_St.dws.sc") - -sc10x <- scMerge(sc10x,sc10x,sc10x.Epi.NE,i.1="Merge_Epi.dws.sc_St.dws.sc",i.2="NE",nm="Merge_Epi.dws.sc_St.dws.sc_NE") - -sc10x.Epi <- scMerge(sc10x.Epi,sc10x.Epi,sc10x.Epi.NE,i.1="Epi.dws.sc",i.2="NE",nm="Epi.dws.sc_NE") - -sc10x <- SetAllIdent(object=sc10x,id="Merge_Epi.dws.sc_St.dws.sc") -sc10x@ident <- factor(sc10x@ident,levels=c("BE","LE","OE_SCGB","OE_KRT13","Fib","SM","Endo","Leu","Unknown")) -postscript("./analysis/tSNE/FINAL/tSNE_FINAL.eps") -plot <- TSNEPlot(object=sc10x,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() - -scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc") -scTables(sc10x,i.1="samples",i.2="Merge_Epi.dws.sc_St.dws.sc_NE") -scTables(sc10x,i.1="Merge_Epi.dws.sc_St.dws.sc_NE",i.2="Merge_Epi.dws.sc_St.dws.sc") - -sctSNECustCol(sc10x,i="Merge_Epi.dws.sc_St.dws.sc",bl=c("BE","LE","OE_SCGB","OE_KRT13"),rd=c("Fib","SM","Endo","Leu","Unknown"),file="D") - -save(list=ls(pattern="sc10x.Stress"),file="./analysis/sc10x.Stress.Rda") -rm(list=ls(pattern="sc10x.Stress")) -save(list=ls(pattern="^genes.deg"),file="./analysis/DEG.Rda") -rm(list=ls(pattern="^genes.deg")) -save(list=ls(pattern="sc10x.Epi"),file="./analysis/sc10x.Epi.Rda") -rm(list=ls(pattern="^sc10x.Epi")) -save(list=ls(pattern="sc10x.St"),file="./analysis/sc10x.St.Rda") -rm(list=ls(pattern="sc10x.St")) -save(list=ls(pattern="^sc10x"),file="./analysis/sc10x.Rda") -rm(list=ls(pattern="^sc10x")) -save.image(file="./analysis/Data.RData") +sc10x.Pd <- sc10x +save(sc10x,file="./analysis/sc10x.Pd.Rda") diff --git a/r.scripts/sc-TissueMapper_RUN.PdPbPc.R b/r.scripts/sc-TissueMapper_RUN.PdPbPc.R index 43fabef..ab650ee 100755 --- a/r.scripts/sc-TissueMapper_RUN.PdPbPc.R +++ b/r.scripts/sc-TissueMapper_RUN.PdPbPc.R @@ -118,7 +118,9 @@ opt=parse_args(OptionParser(option_list=option_list)) rm(option_list) if (opt$lm==0){opt$lm=-Inf} -load("./analysis/sc10x.Rda") +load("./analysis/sc10x.Pd.Rda") +load("./analysis/sc10x.Pb.Rda") +load("./analysis/sc10x.Pc.Rda") for (i in c("Pb","Pc","Pd")){ if (!dir.exists(paste0("./analysis/tSNE/post.stress/",i))){ -- GitLab