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data_mean_mean_new.R
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library(stringr)
library(Seurat)
library(clusterProfiler)
library(patchwork)
library(dplyr)
library(org.Hs.eg.db)
library(tidyverse)
a<-list.files("c:/Users/xjmik/Downloads/data.expression/data/expression/CD4/byDataset/")
y<-list.files("c:/Users/xjmik/Downloads/data.expression/data/expression/CD8/byDataset/")
z<-as.data.frame(t(rep(0,38)))
colnames(z)<-c("a.i.", "length.patient." ,
"mean.d.JMJD1C." , "median.d.JMJD1C." ,
"sum.d.JMJD1C." , "mean.e.IFNG." ,
"median.e.IFNG." , "sum.e.IFNG." ,
"mean.f.STAT3_mean." , "median.f.STAT3_mean.",
"sum.f.STAT3_mean." , "mean.f.STAT3_median.",
"median.f.STAT3_median." ,"sum.f.STAT3_median." ,
"mean.f.STAT3_sum.", "median.f.STAT3_sum.",
"sum.f.STAT3_sum.", "mean.f.PI3K_mean." ,
"median.f.PI3K_mean." , "sum.f.PI3K_mean." ,
"mean.f.PI3K_median." , "median.f.PI3K_median." ,
"sum.f.PI3K_median." , "mean.f.PI3K_sum." ,
"median.f.PI3K_sum." , "sum.f.PI3K_sum." ,
"mean.o.GZMB." , "median.o.GZMB." ,
"sum.o.GZMB." , "mean.p.TNF." ,
"median.p.TNF." , "sum.p.TNF." ,
"mean.q.IFNG." , "median.q.IFNG." ,
"sum.q.IFNG." , "Treg_frequence" ,
"CD8_frequence" , "CD8_ratio" )
for (i in c(1:23,25:length(a))) {
setwd("c:/Users/xjmik/Downloads/data.expression/data/expression/CD4/byDataset/")
b<-readRDS(a[i])
setwd("c:/Users/xjmik/Downloads/data.expression/data/expression/CD8/byDataset/")
x<-readRDS(y[i])
count_B<-b@assays$data@listData$norm_exprs
count_B<-as.matrix(count_B)
count_B<-as.data.frame(count_B)
count_B_metadata<-as.data.frame(b@colData@listData)
count_B<-count_B[,count_B_metadata$cellID]
colnames(count_B)<-count_B_metadata$cellID.uniq
genename<-rownames(count_B)
d<-str_sub(genename[1],1,4)
if(d == "ENSG"){
e<-bitr(geneID = genename,fromType = "ENSEMBL",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
e<-e[!duplicated(e$SYMBOL),]
count_B<-count_B[e$ENSEMBL,]
rownames(count_B)<-e$SYMBOL
}
f<-sort(genename)
if(f[1] == "1"){
g<-bitr(geneID = genename,fromType = "ENTREZID",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
g<-g[!duplicated(g$SYMBOL),]
count_B<-count_B[g$ENTREZID,]
rownames(count_B)<-g$SYMBOL
}
remove(b,e,g,d,f,genename)
e<-readRDS("C:/Users/xjmik/Downloads/data.expression/data/expression/CD4/integration/int.CD4.S35.meta.tb.rds")
rownames(count_B_metadata)<-count_B_metadata$cellID.uniq
h<-intersect(e$cellID.uniq,count_B_metadata$cellID.uniq)
e<-as.data.frame(e)
rownames(e)<-e$cellID.uniq
e_new<-e[h,]
count_B_metadata_new<-count_B_metadata[h,]
metadata<-cbind(e_new,count_B_metadata_new)
remove(e,e_new,count_B_metadata,count_B_metadata_new,h) #
rownames(metadata)<-colnames(count_B)
count_C<-x@assays$data@listData$norm_exprs
count_C<-as.matrix(count_C)
count_C<-as.data.frame(count_C)
count_C_metadata<-as.data.frame(x@colData@listData)
count_C<-count_C[,count_C_metadata$cellID]
colnames(count_C)<-count_C_metadata$cellID.uniq
genename<-rownames(count_C)
d<-str_sub(genename[1],1,4)
if(d == "ENSG"){
e<-bitr(geneID = genename,fromType = "ENSEMBL",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
e<-e[!duplicated(e$SYMBOL),]
count_C<-count_C[e$ENSEMBL,]
rownames(count_C)<-e$SYMBOL
}
f<-sort(genename)
if(f[1] == "1"){
g<-bitr(geneID = genename,fromType = "ENTREZID",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
g<-g[!duplicated(g$SYMBOL),]
count_C<-count_C[g$ENTREZID,]
rownames(count_C)<-g$SYMBOL
}
remove(x,e,g,d,f,genename)
e<-readRDS("C:/Users/xjmik/Downloads/data.expression/data/expression/CD8/integration/int.CD8.S35.meta.tb.rds")
rownames(count_C_metadata)<-count_C_metadata$cellID.uniq
h<-intersect(e$cellID.uniq,count_C_metadata$cellID.uniq)
e<-as.data.frame(e)
rownames(e)<-e$cellID.uniq
e_new<-e[h,]
count_C_metadata_new<-count_C_metadata[h,]
metadata_C<-cbind(e_new,count_C_metadata_new)
remove(e,e_new,count_C_metadata,count_C_metadata_new,h) #
rownames(metadata_C)<-colnames(count_C)
b<-intersect(rownames(count_B),rownames(count_C))
count_B_new<-count_B[b,]
count_C_new<-count_C[b,]
count<-cbind(count_B_new,count_C_new)
remove(count_B,count_B_new,count_C,count_C_new,b)
b<-intersect(colnames(metadata),colnames(metadata_C))
metadata_new<-metadata[,b]
metadata_C_new<-metadata_C[,b]
metadata<-rbind(metadata_new,metadata_C_new)
remove(metadata_C,metadata_C_new,metadata_new,b)
pbmc<-CreateSeuratObject(counts = count,meta.data = metadata)
Idents(pbmc)<[email protected]$loc
remove(count,metadata)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
Idents(pbmc)<[email protected]$meta.cluster
remove(all.genes)
b<-read.table("C:/Users/xjmik/Downloads/STAT3.txt",sep = "\t",header = TRUE)
c<-read.table("C:/Users/xjmik/Downloads/PI3K.txt",sep = "\t",header = TRUE)
b_new<-b[2:length(rownames(b)),]
c_new<-c[2:length(rownames(c)),]
b_new<-intersect(b_new,rownames(pbmc))
c_new<-intersect(c_new,rownames(pbmc))
pbmc<-AddModuleScore(pbmc,features = b_new,name = "STAT3")
pbmc<-AddModuleScore(pbmc,features = c_new,name = "PI3K")
remove(b,c,b_new,c_new)
b<-levels(Idents(pbmc))
c<-b[which(b == "CD4.c18.Treg.RTKN2" | b == "CD4.c19.Treg.S1PR1" | b == "CD4.c20.Treg.TNFRSF9" | b == "CD4.c21.Treg.ISG")]
Treg<-subset(pbmc,ident=c)
d<-b[grep("CD8",b)]
CD8<-subset(pbmc,ident=d)
e<-b[grep("CD4",b)]
CD4<-subset(pbmc,ident=e)
remove(b,c,d,e)
Idents(Treg)<[email protected]$loc
Treg_T<-subset(Treg,ident="T")
Idents(Treg_T)<[email protected]$patient.uid
patient_Treg<-unique([email protected]$patient.uid)
remove(Treg)
Idents(CD8)<[email protected]$loc
CD8_T<-subset(CD8,ident="T")
Idents(CD8_T)<[email protected]$patient.uid
patient_CD8_T<-unique([email protected]$patient.uid)
remove(CD8)
Idents(CD4)<[email protected]$loc
CD4_T<-subset(CD4,ident="T")
Idents(CD4_T)<[email protected]$patient.uid
patient_CD4_T<-unique([email protected]$patient.uid)
remove(CD4)
Idents(pbmc)<[email protected]$loc
pbmc_T<-subset(pbmc,ident="T")
Idents(pbmc_T)<[email protected]$patient.uid
patient_pbmc_T<-unique([email protected]$patient.uid)
remove(pbmc)
patient_1<-intersect(patient_CD8_T,patient_Treg)
patient_2<-intersect(patient_CD4_T,patient_pbmc_T)
patient<-intersect(patient_1,patient_2)
remove(patient_CD8_T,patient_Treg,patient_1,patient_2,patient_CD4_T,patient_pbmc_T)
for (j in 1:length(patient)) {
assign(paste("Treg_",j,"_TIL",sep = ""),subset(Treg_T,idents = patient[j]))
assign("b",slot(get(paste("Treg_",j,"_TIL",sep = "")),"assays"))
assign("c",as.data.frame(t(as.data.frame(b$RNA@data))))
for (r in 1:length(colnames(c))) {
noise<-rnorm(n=length(x=c[,r]))/1e+05
c[,r]<-c[,r]+noise
}
remove(noise,r)
assign("d",dplyr::select(c,one_of("JMJD1C")))
assign("e",dplyr::select(c,one_of("IFNG")))
assign("f",slot(get(paste("Treg_",j,"_TIL",sep = "")),"meta.data"))
STAT3<-dplyr::select(f,starts_with("STAT3"))
for (r in 1:length(colnames(STAT3))) {
noise<-rnorm(n=length(x=STAT3[,r]))/1e+05
STAT3[,r]<-STAT3[,r]+noise
}
remove(noise,r)
f$STAT3_mean<-apply(STAT3,MARGIN = 1,mean)
f$STAT3_median<-apply(STAT3,MARGIN = 1,median)
f$STAT3_sum<-apply(STAT3,MARGIN = 1,sum)
PI3K<-dplyr::select(f,starts_with("PI3K"))
for (r in 1:length(colnames(PI3K))) {
noise<-rnorm(n=length(x=PI3K[,r]))/1e+05
PI3K[,r]<-PI3K[,r]+noise
}
remove(noise,r)
f$PI3K_mean<-apply(PI3K,MARGIN = 1,mean)
f$PI3K_median<-apply(PI3K,MARGIN = 1,median)
f$PI3K_sum<-apply(PI3K,MARGIN = 1,sum)
remove(b,c,PI3K,STAT3)
assign("g",length(colnames(get(paste("Treg_",j,"_TIL",sep = "")))))
assign(paste("CD4_",j,"_TIL",sep = ""),subset(CD4_T,idents = patient[j]))
assign("h",length(colnames(get(paste("CD4_",j,"_TIL",sep = "")))))
assign(paste("pbmc_",j,"_TIL",sep = ""),subset(pbmc_T,idents = patient[j]))
assign("k",length(colnames(get(paste("pbmc_",j,"_TIL",sep = "")))))
assign(paste("CD8_",j,"_TIL",sep = ""),subset(CD8_T,idents = patient[j]))
assign("l",length(colnames(get(paste("CD8_",j,"_TIL",sep = "")))))
assign("m",slot(get(paste("CD8_",j,"_TIL",sep = "")),"assays"))
assign("n",as.data.frame(t(as.data.frame(m$RNA@data))))
for (r in 1:length(colnames(n))) {
noise<-rnorm(n=length(x=n[,r]))/1e+05
n[,r]<-n[,r]+noise
}
remove(noise,r)
assign("o",dplyr::select(n,one_of("GZMB")))
assign("p",dplyr::select(n,one_of("TNF")))
assign("q",dplyr::select(n,one_of("IFNG")))
remove(m,n)
Treg_frequence<-g/h
CD8_frequence<-l/k
CD8_ratio<-l/g
remove(g,h,k,l)
assign(paste("scaldata_",j,"_TIL",sep = ""),data.frame(a[i],length(patient),
mean(d$JMJD1C),median(d$JMJD1C),sum(d$JMJD1C),
mean(e$IFNG),median(e$IFNG),sum(e$IFNG),
mean(f$STAT3_mean),median(f$STAT3_mean),sum(f$STAT3_mean),
mean(f$STAT3_median),median(f$STAT3_median),sum(f$STAT3_median),
mean(f$STAT3_sum),median(f$STAT3_sum),sum(f$STAT3_sum),
mean(f$PI3K_mean),median(f$PI3K_mean),sum(f$PI3K_mean),
mean(f$PI3K_median),median(f$PI3K_median),sum(f$PI3K_median),
mean(f$PI3K_sum),median(f$PI3K_sum),sum(f$PI3K_sum),
mean(o$GZMB),median(o$GZMB),sum(o$GZMB),
mean(p$TNF),median(p$TNF),sum(p$TNF),
mean(q$IFNG),median(q$IFNG),sum(q$IFNG),
Treg_frequence,CD8_frequence,CD8_ratio))
remove(list =paste("Treg_",j,"_TIL",sep = ""))
remove(list =paste("CD4_",j,"_TIL",sep = ""))
remove(list =paste("CD8_",j,"_TIL",sep = ""))
remove(list =paste("pbmc_",j,"_TIL",sep = ""))
remove(d,e,f,o,p,q,CD8_frequence,CD8_ratio,Treg_frequence)
}
scaldata<-scaldata_1_TIL
if (length(patient)>=2){
for (j in 2:length(patient)) {
scaldata<-rbind(scaldata,get(paste("scaldata_",j,"_TIL",sep = "")))
remove(list = paste("scaldata_",j,"_TIL",sep = ""))
}
}
remove(scaldata_1_TIL,j,CD4_T,CD8_T,pbmc_T,Treg_T)
colnames(scaldata)<-c("a.i.", "length.patient." ,
"mean.d.JMJD1C." , "median.d.JMJD1C." ,
"sum.d.JMJD1C." , "mean.e.IFNG." ,
"median.e.IFNG." , "sum.e.IFNG." ,
"mean.f.STAT3_mean." , "median.f.STAT3_mean.",
"sum.f.STAT3_mean." , "mean.f.STAT3_median.",
"median.f.STAT3_median." ,"sum.f.STAT3_median." ,
"mean.f.STAT3_sum.", "median.f.STAT3_sum.",
"sum.f.STAT3_sum.", "mean.f.PI3K_mean." ,
"median.f.PI3K_mean." , "sum.f.PI3K_mean." ,
"mean.f.PI3K_median." , "median.f.PI3K_median." ,
"sum.f.PI3K_median." , "mean.f.PI3K_sum." ,
"median.f.PI3K_sum." , "sum.f.PI3K_sum." ,
"mean.o.GZMB." , "median.o.GZMB." ,
"sum.o.GZMB." , "mean.p.TNF." ,
"median.p.TNF." , "sum.p.TNF." ,
"mean.q.IFNG." , "median.q.IFNG." ,
"sum.q.IFNG." , "Treg_frequence" ,
"CD8_frequence" , "CD8_ratio" )
z<-rbind(z,scaldata)
}
write.table(z,"c:/Users/xjmik/Desktop/Rawdata.txt",sep = "\t")
#b<-cor(scaldata$JMJD1C,scaldata$IFNG,method = "spearman")
#c<-cor.test(scaldata$JMJD1C,scaldata$IFNG,method = "spearman",exact = TRUE)
#d<-data.frame(b,c$p.value,a[i],i,length(rownames(scaldata)))
#colnames(d)<-c("R","P","Name","i","Sample_Size")
#z<-rbind(z,d)
#remove(b,c,d,scaldata,Treg_T)
setwd("c:/Users/xjmik/Downloads/data.expression/data/expression/CD4/byDataset/")
b<-readRDS(a[24])
setwd("c:/Users/xjmik/Downloads/data.expression/data/expression/CD8/byDataset/")
x<-readRDS(y[24])
count_B<-b@assays@data@listData$norm_exprs
count_B<-as.matrix(count_B)
count_B<-as.data.frame(count_B)
count_B_metadata<-as.data.frame(b@colData@listData)
count_B<-count_B[,count_B_metadata$cellID]
colnames(count_B)<-count_B_metadata$cellID.uniq
genename<-rownames(count_B)
d<-str_sub(genename[1],1,4)
if(d == "ENSG"){
e<-bitr(geneID = genename,fromType = "ENSEMBL",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
e<-e[!duplicated(e$SYMBOL),]
count_B<-count_B[e$ENSEMBL,]
rownames(count_B)<-e$SYMBOL
}
f<-sort(genename)
if(f[1] == "1"){
g<-bitr(geneID = genename,fromType = "ENTREZID",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
g<-g[!duplicated(g$SYMBOL),]
count_B<-count_B[g$ENTREZID,]
rownames(count_B)<-g$SYMBOL
}
remove(b,e,g,d,f,genename)
e<-readRDS("C:/Users/xjmik/Downloads/data.expression/data/expression/CD4/integration/int.CD4.S35.meta.tb.rds")
rownames(count_B_metadata)<-count_B_metadata$cellID.uniq
h<-intersect(e$cellID.uniq,count_B_metadata$cellID.uniq)
e<-as.data.frame(e)
rownames(e)<-e$cellID.uniq
e_new<-e[h,]
count_B_metadata_new<-count_B_metadata[h,]
metadata<-cbind(e_new,count_B_metadata_new)
remove(e,e_new,count_B_metadata,count_B_metadata_new,h) #
rownames(metadata)<-colnames(count_B)
count_C<-x@assays@data@listData$norm_exprs
count_C<-as.matrix(count_C)
count_C<-as.data.frame(count_C)
count_C_metadata<-as.data.frame(x@colData@listData)
count_C<-count_C[,count_C_metadata$cellID]
colnames(count_C)<-count_C_metadata$cellID.uniq
genename<-rownames(count_C)
d<-str_sub(genename[1],1,4)
if(d == "ENSG"){
e<-bitr(geneID = genename,fromType = "ENSEMBL",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
e<-e[!duplicated(e$SYMBOL),]
count_C<-count_C[e$ENSEMBL,]
rownames(count_C)<-e$SYMBOL
}
f<-sort(genename)
if(f[1] == "1"){
g<-bitr(geneID = genename,fromType = "ENTREZID",toType = "SYMBOL",OrgDb = org.Hs.eg.db,drop = TRUE)
g<-g[!duplicated(g$SYMBOL),]
count_C<-count_C[g$ENTREZID,]
rownames(count_C)<-g$SYMBOL
}
remove(x,e,g,d,f,genename)
e<-readRDS("C:/Users/xjmik/Downloads/data.expression/data/expression/CD8/integration/int.CD8.S35.meta.tb.rds")
rownames(count_C_metadata)<-count_C_metadata$cellID.uniq
h<-intersect(e$cellID.uniq,count_C_metadata$cellID.uniq)
e<-as.data.frame(e)
rownames(e)<-e$cellID.uniq
e_new<-e[h,]
count_C_metadata_new<-count_C_metadata[h,]
metadata_C<-cbind(e_new,count_C_metadata_new)
remove(e,e_new,count_C_metadata,count_C_metadata_new,h) #
rownames(metadata_C)<-colnames(count_C)
b<-intersect(rownames(count_B),rownames(count_C))
count_B_new<-count_B[b,]
count_C_new<-count_C[b,]
count<-cbind(count_B_new,count_C_new)
remove(count_B,count_B_new,count_C,count_C_new,b)
b<-intersect(colnames(metadata),colnames(metadata_C))
metadata_new<-metadata[,b]
metadata_C_new<-metadata_C[,b]
metadata<-rbind(metadata_new,metadata_C_new)
remove(metadata_C,metadata_C_new,metadata_new,b)
pbmc<-CreateSeuratObject(counts = count,meta.data = metadata)
Idents(pbmc)<[email protected]$loc
remove(count,metadata)
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes)
Idents(pbmc)<[email protected]$meta.cluster
remove(all.genes)
b<-read.table("C:/Users/xjmik/Downloads/STAT3.txt",sep = "\t",header = TRUE)
c<-read.table("C:/Users/xjmik/Downloads/PI3K.txt",sep = "\t",header = TRUE)
b_new<-b[2:length(rownames(b)),]
c_new<-c[2:length(rownames(c)),]
b_new<-intersect(b_new,rownames(pbmc))
c_new<-intersect(c_new,rownames(pbmc))
pbmc<-AddModuleScore(pbmc,features = b_new,name = "STAT3")
pbmc<-AddModuleScore(pbmc,features = c_new,name = "PI3K")
remove(b,c,b_new,c_new)
b<-levels(Idents(pbmc))
c<-b[which(b == "CD4.c18.Treg.RTKN2" | b == "CD4.c19.Treg.S1PR1" | b == "CD4.c20.Treg.TNFRSF9" | b == "CD4.c21.Treg.ISG")]
Treg<-subset(pbmc,ident=c)
d<-b[grep("CD8",b)]
CD8<-subset(pbmc,ident=d)
e<-b[grep("CD4",b)]
CD4<-subset(pbmc,ident=e)
remove(b,c,d,e)
Idents(Treg)<[email protected]$loc
Treg_T<-subset(Treg,ident="T")
Idents(Treg_T)<[email protected]$patient.uid
patient_Treg<-unique([email protected]$patient.uid)
remove(Treg)
Idents(CD8)<[email protected]$loc
CD8_T<-subset(CD8,ident="T")
Idents(CD8_T)<[email protected]$patient.uid
patient_CD8_T<-unique([email protected]$patient.uid)
remove(CD8)
Idents(CD4)<[email protected]$loc
CD4_T<-subset(CD4,ident="T")
Idents(CD4_T)<[email protected]$patient.uid
patient_CD4_T<-unique([email protected]$patient.uid)
remove(CD4)
Idents(pbmc)<[email protected]$loc
pbmc_T<-subset(pbmc,ident="T")
Idents(pbmc_T)<[email protected]$patient.uid
patient_pbmc_T<-unique([email protected]$patient.uid)
remove(pbmc)
patient_1<-intersect(patient_CD8_T,patient_Treg)
patient_2<-intersect(patient_CD4_T,patient_pbmc_T)
patient<-intersect(patient_1,patient_2)
remove(patient_CD8_T,patient_Treg,patient_1,patient_2,patient_CD4_T,patient_pbmc_T)
for (j in 1:length(patient)) {
assign(paste("Treg_",j,"_TIL",sep = ""),subset(Treg_T,idents = patient[j]))
assign("b",slot(get(paste("Treg_",j,"_TIL",sep = "")),"assays"))
assign("c",as.data.frame(t(as.data.frame(b$RNA@data))))
for (r in 1:length(colnames(c))) {
noise<-rnorm(n=length(x=c[,r]))/1e+05
c[,r]<-c[,r]+noise
}
remove(noise,r)
assign("d",dplyr::select(c,one_of("JMJD1C")))
assign("e",dplyr::select(c,one_of("IFNG")))
assign("f",slot(get(paste("Treg_",j,"_TIL",sep = "")),"meta.data"))
STAT3<-dplyr::select(f,starts_with("STAT3"))
for (r in 1:length(colnames(STAT3))) {
noise<-rnorm(n=length(x=STAT3[,r]))/1e+05
STAT3[,r]<-STAT3[,r]+noise
}
remove(noise,r)
f$STAT3_mean<-apply(STAT3,MARGIN = 1,mean)
f$STAT3_median<-apply(STAT3,MARGIN = 1,median)
f$STAT3_sum<-apply(STAT3,MARGIN = 1,sum)
PI3K<-dplyr::select(f,starts_with("PI3K"))
for (r in 1:length(colnames(PI3K))) {
noise<-rnorm(n=length(x=PI3K[,r]))/1e+05
PI3K[,r]<-PI3K[,r]+noise
}
remove(noise,r)
f$PI3K_mean<-apply(PI3K,MARGIN = 1,mean)
f$PI3K_median<-apply(PI3K,MARGIN = 1,median)
f$PI3K_sum<-apply(PI3K,MARGIN = 1,sum)
remove(b,c,PI3K,STAT3)
assign("g",length(colnames(get(paste("Treg_",j,"_TIL",sep = "")))))
assign(paste("CD4_",j,"_TIL",sep = ""),subset(CD4_T,idents = patient[j]))
assign("h",length(colnames(get(paste("CD4_",j,"_TIL",sep = "")))))
assign(paste("pbmc_",j,"_TIL",sep = ""),subset(pbmc_T,idents = patient[j]))
assign("k",length(colnames(get(paste("pbmc_",j,"_TIL",sep = "")))))
assign(paste("CD8_",j,"_TIL",sep = ""),subset(CD8_T,idents = patient[j]))
assign("l",length(colnames(get(paste("CD8_",j,"_TIL",sep = "")))))
assign("m",slot(get(paste("CD8_",j,"_TIL",sep = "")),"assays"))
assign("n",as.data.frame(t(as.data.frame(m$RNA@data))))
for (r in 1:length(colnames(n))) {
noise<-rnorm(n=length(x=n[,r]))/1e+05
n[,r]<-n[,r]+noise
}
remove(noise,r)
assign("o",dplyr::select(n,one_of("GZMB")))
assign("p",dplyr::select(n,one_of("TNF")))
assign("q",dplyr::select(n,one_of("IFNG")))
remove(m,n)
Treg_frequence<-g/h
CD8_frequence<-l/k
CD8_ratio<-l/g
remove(g,h,k,l)
assign(paste("scaldata_",j,"_TIL",sep = ""),data.frame(a[24],length(patient),
mean(d$JMJD1C),median(d$JMJD1C),sum(d$JMJD1C),
mean(e$IFNG),median(e$IFNG),sum(e$IFNG),
mean(f$STAT3_mean),median(f$STAT3_mean),sum(f$STAT3_mean),
mean(f$STAT3_median),median(f$STAT3_median),sum(f$STAT3_median),
mean(f$STAT3_sum),median(f$STAT3_sum),sum(f$STAT3_sum),
mean(f$PI3K_mean),median(f$PI3K_mean),sum(f$PI3K_mean),
mean(f$PI3K_median),median(f$PI3K_median),sum(f$PI3K_median),
mean(f$PI3K_sum),median(f$PI3K_sum),sum(f$PI3K_sum),
mean(o$GZMB),median(o$GZMB),sum(o$GZMB),
mean(p$TNF),median(p$TNF),sum(p$TNF),
mean(q$IFNG),median(q$IFNG),sum(q$IFNG),
Treg_frequence,CD8_frequence,CD8_ratio))
remove(list =paste("Treg_",j,"_TIL",sep = ""))
remove(list =paste("CD4_",j,"_TIL",sep = ""))
remove(list =paste("CD8_",j,"_TIL",sep = ""))
remove(list =paste("pbmc_",j,"_TIL",sep = ""))
remove(d,e,f,o,p,q,CD8_frequence,CD8_ratio,Treg_frequence)
}
scaldata<-scaldata_1_TIL
if (length(patient)>=2){
for (j in 2:length(patient)) {
scaldata<-rbind(scaldata,get(paste("scaldata_",j,"_TIL",sep = "")))
remove(list = paste("scaldata_",j,"_TIL",sep = ""))
}
}
remove(scaldata_1_TIL,j,CD4_T,CD8_T,pbmc_T,Treg_T)
colnames(scaldata)<-c("a.i.", "length.patient." ,
"mean.d.JMJD1C." , "median.d.JMJD1C." ,
"sum.d.JMJD1C." , "mean.e.IFNG." ,
"median.e.IFNG." , "sum.e.IFNG." ,
"mean.f.STAT3_mean." , "median.f.STAT3_mean.",
"sum.f.STAT3_mean." , "mean.f.STAT3_median.",
"median.f.STAT3_median." ,"sum.f.STAT3_median." ,
"mean.f.STAT3_sum.", "median.f.STAT3_sum.",
"sum.f.STAT3_sum.", "mean.f.PI3K_mean." ,
"median.f.PI3K_mean." , "sum.f.PI3K_mean." ,
"mean.f.PI3K_median." , "median.f.PI3K_median." ,
"sum.f.PI3K_median." , "mean.f.PI3K_sum." ,
"median.f.PI3K_sum." , "sum.f.PI3K_sum." ,
"mean.o.GZMB." , "median.o.GZMB." ,
"sum.o.GZMB." , "mean.p.TNF." ,
"median.p.TNF." , "sum.p.TNF." ,
"mean.q.IFNG." , "median.q.IFNG." ,
"sum.q.IFNG." , "Treg_frequence" ,
"CD8_frequence" , "CD8_ratio" )
write.table(scaldata,"c:/Users/xjmik/Desktop/Rawdata2.txt",sep = "\t")