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Commit 84c4e944 authored by Gervaise Henry's avatar Gervaise Henry :cowboy:
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Finalize (?) all code for PdPbPc to run

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bash.scripts/sc_TissueMapper-PdPbPc.sh 0 → 100644
+ 17
− 0
View file @ 84c4e944
#!/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
r.scripts/sc-TissueMapper_RUN.PdPbPc.Pb.R
+ 10
− 184
View file @ 84c4e944
......@@ -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")
r.scripts/sc-TissueMapper_RUN.PdPbPc.Pc.R
+ 3
− 175
View file @ 84c4e944
......@@ -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")
r.scripts/sc-TissueMapper_RUN.PdPbPc.Pd.R
+ 10
− 184
View file @ 84c4e944
......@@ -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")
r.scripts/sc-TissueMapper_RUN.PdPbPc.R
+ 3
− 1
View file @ 84c4e944
......@@ -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))){
......
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