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CD45_Stat3.R
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CD45_Stat3.R
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#### CD45 Version7: use exon expression as cutoff and splice junction for validate
library(data.table)
multmerge = function(mypath){
#need to change the pattern accordingly !!!
filenames=list.files(path=mypath, pattern="txt", full.names=TRUE)
datalist = lapply(filenames, function(x){
tmp<-fread(x,header=TRUE, select = 4, sep = "\t")
setnames(tmp, "median",tail(strsplit(x,"/")[[1]],n=1))
return(tmp)})
Reduce(function(x,y) {cbind(x,y)}, datalist)
}
path<-file.path("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_v4/Exon37_exp/")
system.time(median_merge<-multmerge(path))
median_merge <- as.data.frame(median_merge)
colnames(median_merge) <- substr(colnames(median_merge),1,10)
colnames(median_merge) <- gsub(colnames(median_merge), pattern="UCB3", replacement="UCB4")
colnames(median_merge) <- gsub(colnames(median_merge), pattern="UCB1", replacement="UCB3")
colnames(median_merge) <- gsub(colnames(median_merge), pattern="UCB5", replacement="UCB1")
row.names(median_merge) <- paste("E", 3:7, sep = "")
median_merge <- t(median_merge)
median_merge <- data.frame(median_merge)
median_merge$Exon_Sum <- as.numeric(rowSums(median_merge))
hist(log10(median_merge$Exon_Sum), breaks=200)
median_merge$difE3E7 <- median_merge$E3/(median_merge$E3+median_merge$E7)
median_merge[is.na(median_merge)] <- 0
hist(median_merge$difE3E7, breaks=200)
hist(log10(median_merge$Exon_Sum), breaks=200)
abline(v = 1.3, col = 2)
sum(log10(median_merge$Exon_Sum)>=1.3)
[1] 2663
plot(density(median_merge$difE3E7, adjust=.5))
abline(v = .4, col = 2)
abline(v = .75, col = 2)
sum(median_merge$difE3E7>=.4&median_merge$difE3E7<=.75)
[1] 2364
sum(log10(median_merge$Exon_Sum)>=1.3 & median_merge$difE3E7>=.4 & median_merge$difE3E7<=.75)
[1] 2109
exon_exp_tu <- median_merge[log10(median_merge$Exon_Sum)>=1.3 & median_merge$difE3E7>=.4 & median_merge$difE3E7<=.75, ]
hist(log10(exon_exp_tu$Exon_Sum), breaks=200)
plot(density(exon_exp_tu$difE3E7, adjust=.5))
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Exon_expression_cutoff.pdf")
hist(log10(median_merge$Exon_Sum), breaks=200, xlab = "log10(Read counts of CD45 exon3 to exon7)", main = "")
abline(v = 1.3, col = 2)
plot(density(median_merge$difE3E7, adjust=.5), xlab = "exon3/(exon3+exon7)", main = "")
abline(v = .4, col = 2)
abline(v = .75, col = 2)
dev.off()
#### CD45 of salmon
library(data.table)
multmerge = function(mypath){
#need to change the pattern accordingly !!!
filenames=list.files(path=mypath, full.names=TRUE)
datalist = lapply(paste(filenames,"quant.sf",sep = "/"), function(x){
tmp<-fread(x, header=TRUE, select = 5, sep = "\t")
setnames(tmp, "NumReads",strsplit(x,"/")[[1]][9])
return(tmp)})
Reduce(function(x,y) {cbind(x,y)}, datalist)
}
path<-file.path("/mnt/data5/BGI/UCB/xiaqiuqi/result/3039_cd45_result")
system.time(cov_merge<-multmerge(path))
cov_merge <- as.data.frame(cov_merge)
row.names(cov_merge) <- c("RA","RAB","RABC","RAC","RB","RBC","RC","RO")
colnames(cov_merge) <- substr(colnames(cov_merge), nchar(colnames(cov_merge))-9, nchar(colnames(cov_merge)))
load("/mnt/data5/BGI/UCB/ExpMat_NewID/Seurat.RData")
tsne <- as.data.frame(Combine@dr$tsne@cell.embeddings)
cov_merge[,row.names(tsne)] -> cov_tu
#cov_tu[cov_tu<20] <- 0
Cell_type <- read.table("/mnt/data5/BGI/UCB/ExpMat_NewID/Cell_type.txt", header = F, row.names = 1, sep = "\t", stringsAsFactors=F)
colnames(Cell_type) <- "CellType"
Cell_type <- data.frame(CellType = Cell_type[order(Cell_type),], row.names = row.names(Cell_type)[order(Cell_type)])
Cell_type$Individual <- substr(row.names(Cell_type), 1, 4)
sum(colnames(cov_tu) %in% row.names(exon_exp_tu))
# [1] 1784
1784/3039
# [1] 0.5870352
#### Cutoff use exon expression
cov_tu[, !colnames(cov_tu) %in% row.names(exon_exp_tu)] <- 0
#### validate use splice junction
load("/mnt/data5/BGI/UCB/tangchao/data/SJ/SJ_merged_raw_te.RData")
library(data.table)
SJ34 = "1:198692374-198696711"
SJ35 = "1:198692374-198699563"
SJ45 = "1:198696910-198699563"
SJ46 = "1:198696910-198702386"
SJ56 = "1:198699705-198702386"
SJ57 = "1:198699705-198703297"
SJ67 = "1:198702531-198703297"
SJ47 = "1:198696910-198703297"
SJ36 = "1:198692374-198702386"
SJ37 = "1:198692374-198703297"
as.data.frame(te[.(c(SJ34,SJ35,SJ45,SJ46,SJ56,SJ57,SJ67,SJ47,SJ36,SJ37))])[,-1] -> CD45_SJ
colnames(CD45_SJ) <- substr(colnames(CD45_SJ),1,10)
row.names(CD45_SJ) <- c("SJ34","SJ35","SJ45","SJ46","SJ56","SJ57","SJ67","SJ47","SJ36","SJ37")
CD45_SJ <- CD45_SJ[,colnames(cov_tu)]
cov_tu["RA", apply(CD45_SJ[c("SJ34","SJ47"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RAB", apply(CD45_SJ[c("SJ34","SJ45","SJ57"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RABC", apply(CD45_SJ[c("SJ34","SJ45","SJ56","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RAC", apply(CD45_SJ[c("SJ34","SJ46","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RB", apply(CD45_SJ[c("SJ35","SJ57"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RBC", apply(CD45_SJ[c("SJ35","SJ56","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RC", apply(CD45_SJ[c("SJ36","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RO", apply(CD45_SJ[c("SJ37"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu[cov_tu<20] <- 0
table(colSums(cov_tu>0))
# 0 1 2 3 4 5
# 1284 795 536 188 225 11
cov_tu[cov_tu<50] <- 0
table(colSums(cov_tu>0))
# 0 1 2 3 4 5
# 1343 940 437 142 173 4
save(cov_tu, file = "/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/cov_tu.RData")
library(ggplot2)
t(t(cov_tu)[order(t(cov_tu)[,1]),]) -> CD45_R_r_tab1
f1 <- ggplot(data = tsne[colnames(CD45_R_r_tab1),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab1[1,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab1)[1])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,2]),]) -> CD45_R_r_tab2
f2 <- ggplot(data = tsne[colnames(CD45_R_r_tab2),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab2[2,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab2)[2])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,3]),]) -> CD45_R_r_tab3
f3 <- ggplot(data = tsne[colnames(CD45_R_r_tab3),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab3[3,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab3)[3])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,4]),]) -> CD45_R_r_tab4
f4 <- ggplot(data = tsne[colnames(CD45_R_r_tab4),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab4[4,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab4)[4])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,5]),]) -> CD45_R_r_tab5
f5 <- ggplot(data = tsne[colnames(CD45_R_r_tab5),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab5[5,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab5)[5])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,6]),]) -> CD45_R_r_tab6
f6 <- ggplot(data = tsne[colnames(CD45_R_r_tab6),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab6[6,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab6)[6])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,7]),]) -> CD45_R_r_tab7
f7 <- ggplot(data = tsne[colnames(CD45_R_r_tab7),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab7[7,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab7)[7])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,8]),]) -> CD45_R_r_tab8
f8 <- ggplot(data = tsne[colnames(CD45_R_r_tab8),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab8[8,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab8)[8])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/CD45_RABC_v7.pdf", height = 6,width = 16)
library(cowplot)
plot_grid(f1, f2, f3, f4, f5, f6, f7, f8, nrow = 2)
dev.off()
test <- cov_tu[c(1,4,7),]
test2 <- data.frame(colSums(test))
test3 <- do.call(rbind,apply(test,2,function(x) row.names(test)[which(x>0)]))
test4 <- merge(test2,test3,by=0, all.x=T)
test4[,3] <- as.numeric(test4[,3])
test4[is.na(test4)] <- 0
test4[order(test4[,2]),] -> test4
f9 <- ggplot(data = tsne[test4[,1],], aes(x = tSNE_1, y = tSNE_2, shape = as.factor(test4[,3])))+
geom_point(aes(colour = log10(as.numeric(test4[,2]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
scale_shape(solid = TRUE) +
ggtitle("")+
scale_shape_manual(values = c(19,4,5,6),labels=c("Other","RA","RAC","RC"))+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/CD45_RABC_v7_f5.pdf", height = 6, width = 12)
library(cowplot)
plot_grid(f2, f3, f5, f6, f8, f9, nrow = 2)
dev.off()
#### Only plot the major isoform
for(i in 1:ncol(cov_tu)){
cov_tu[,i][-which.max(cov_tu[,i])] <- 0
}
t(t(cov_tu)[order(t(cov_tu)[,1]),]) -> CD45_R_r_tab1
f1 <- ggplot(data = tsne[colnames(CD45_R_r_tab1),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab1[1,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab1)[1])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,2]),]) -> CD45_R_r_tab2
f2 <- ggplot(data = tsne[colnames(CD45_R_r_tab2),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab2[2,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab2)[2])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,3]),]) -> CD45_R_r_tab3
f3 <- ggplot(data = tsne[colnames(CD45_R_r_tab3),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab3[3,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab3)[3])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,4]),]) -> CD45_R_r_tab4
f4 <- ggplot(data = tsne[colnames(CD45_R_r_tab4),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab4[4,]+1))), size = 1)+
scale_colour_gradient(high = 'Grey',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab4)[4])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,5]),]) -> CD45_R_r_tab5
f5 <- ggplot(data = tsne[colnames(CD45_R_r_tab5),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab5[5,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab5)[5])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,6]),]) -> CD45_R_r_tab6
f6 <- ggplot(data = tsne[colnames(CD45_R_r_tab6),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab6[6,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab6)[6])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,7]),]) -> CD45_R_r_tab7
f7 <- ggplot(data = tsne[colnames(CD45_R_r_tab7),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab7[7,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab7)[7])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,8]),]) -> CD45_R_r_tab8
f8 <- ggplot(data = tsne[colnames(CD45_R_r_tab8),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab8[8,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab8)[8])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/CD45_RABC_v7_2.pdf", height = 6,width = 16)
library(cowplot)
plot_grid(f1, f2, f3, f4, f5, f6, f7, f8, nrow = 2)
dev.off()
test <- cov_tu[c(1,4,7),]
test2 <- data.frame(colSums(test))
test3 <- do.call(rbind,apply(test,2,function(x) row.names(test)[which(x>0)]))
test4 <- merge(test2,test3,by=0, all.x=T)
test4[,3] <- as.numeric(test4[,3])
test4[is.na(test4)] <- 0
test4[order(test4[,2]),] -> test4
f9 <- ggplot(data = tsne[test4[,1],], aes(x = tSNE_1, y = tSNE_2, shape = as.factor(test4[,3])))+
geom_point(aes(colour = log10(as.numeric(test4[,2]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
scale_shape(solid = TRUE) +
ggtitle("")+
scale_shape_manual(values = c(19,4,6),labels=c("Other","RA","RC"))+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/CD45_RABC_v7_2_f5.pdf", height = 6, width = 12)
library(cowplot)
plot_grid(f2, f3, f5, f6, f8, f9, nrow = 2)
dev.off()
cov_merge[,row.names(tsne)] -> cov_tu
cov_tu[, !colnames(cov_tu) %in% row.names(exon_exp_tu)] <- 0
cov_tu["RA", apply(CD45_SJ[c("SJ34","SJ47"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RAB", apply(CD45_SJ[c("SJ34","SJ45","SJ57"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RABC", apply(CD45_SJ[c("SJ34","SJ45","SJ56","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RAC", apply(CD45_SJ[c("SJ34","SJ46","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RB", apply(CD45_SJ[c("SJ35","SJ57"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RBC", apply(CD45_SJ[c("SJ35","SJ56","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RC", apply(CD45_SJ[c("SJ36","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RO", apply(CD45_SJ[c("SJ37"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu[cov_tu<50] <- 0
table(colSums(cov_tu>0))
# 0 1 2 3 4 5
# 1343 940 437 142 173 4
#### Only plot the cells have only one isoform
for(i in 1:ncol(cov_tu)){
if(sum(cov_tu[,i] > 0 )>1){
cov_tu[,i] <- 0
}
}
t(t(cov_tu)[order(t(cov_tu)[,1]),]) -> CD45_R_r_tab1
f1 <- ggplot(data = tsne[colnames(CD45_R_r_tab1),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab1[1,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab1)[1])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,2]),]) -> CD45_R_r_tab2
f2 <- ggplot(data = tsne[colnames(CD45_R_r_tab2),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab2[2,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab2)[2])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,3]),]) -> CD45_R_r_tab3
f3 <- ggplot(data = tsne[colnames(CD45_R_r_tab3),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab3[3,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab3)[3])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,4]),]) -> CD45_R_r_tab4
f4 <- ggplot(data = tsne[colnames(CD45_R_r_tab4),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab4[4,]+1))), size = 1)+
scale_colour_gradient(high = 'Grey',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab4)[4])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,5]),]) -> CD45_R_r_tab5
f5 <- ggplot(data = tsne[colnames(CD45_R_r_tab5),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab5[5,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab5)[5])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,6]),]) -> CD45_R_r_tab6
f6 <- ggplot(data = tsne[colnames(CD45_R_r_tab6),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab6[6,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab6)[6])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,7]),]) -> CD45_R_r_tab7
f7 <- ggplot(data = tsne[colnames(CD45_R_r_tab7),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab7[7,]+1))), size = 1)+
scale_colour_gradient(high = 'Grey',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab7)[7])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
t(t(cov_tu)[order(t(cov_tu)[,8]),]) -> CD45_R_r_tab8
f8 <- ggplot(data = tsne[colnames(CD45_R_r_tab8),], aes(x = tSNE_1, y = tSNE_2))+
geom_point(aes(colour = log10(as.numeric(CD45_R_r_tab8[8,]+1))), size = 1)+
scale_colour_gradient(high = 'red',low = 'Grey')+
ggtitle(row.names(CD45_R_r_tab8)[8])+
theme(legend.title = element_blank(),
axis.title = element_blank(),
axis.text= element_blank(),
axis.line = element_blank(),
axis.ticks = element_blank(),
panel.border = element_blank(),
panel.background = element_blank())
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/CD45_RABC_v7_3.pdf", height = 6,width = 16)
library(cowplot)
plot_grid(f1, f2, f3, f4, f5, f6, f7, f8, nrow = 2)
dev.off()
cov_merge[,row.names(tsne)] -> cov_tu
cov_tu[, !colnames(cov_tu) %in% row.names(exon_exp_tu)] <- 0
cov_tu["RA", apply(CD45_SJ[c("SJ34","SJ47"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RAB", apply(CD45_SJ[c("SJ34","SJ45","SJ57"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RABC", apply(CD45_SJ[c("SJ34","SJ45","SJ56","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RAC", apply(CD45_SJ[c("SJ34","SJ46","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RB", apply(CD45_SJ[c("SJ35","SJ57"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RBC", apply(CD45_SJ[c("SJ35","SJ56","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RC", apply(CD45_SJ[c("SJ36","SJ67"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu["RO", apply(CD45_SJ[c("SJ37"),], 2, function(x) sum(is.na(x))!=0)] <- 0
cov_tu[cov_tu<50] <- 0
table(colSums(cov_tu>0))
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bar chart of cell isoform numbers.pdf")
x = barplot(table(apply(cov_tu,2,function(x) sum(x>0))), ylim = c(0,1500),
ylab = "No. Cells", col = "white", xlab = "No. Isoforms")
n <- table(apply(cov_tu,2,function(x) sum(x>0)))
text(x = x, y = n, labels = n, cex = .8, pos = 3)
dev.off()
cov_tu[,row.names(Cell_type)] -> cov_tu
cov_tu_p <- apply(cov_tu, 2, function(x) x/sum(x))
cov_tu_p[is.na(cov_tu_p)] <- 0
major_isoform_p <- apply(cov_tu_p, 2, max)
identical(row.names(Cell_type), names(major_isoform_p))
#[1] TRUE
Cell_type$P_major <- major_isoform_p
Cell_type$P_major2 <- Cell_type$P_major
Cell_type[Cell_type$P_major2==0,"P_major2"] <-NA
ggplot(data = Cell_type, aes(x = major_isoform_p, colour = CellType))+
geom_density()
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Density plot of major isoform percentage.pdf")
ggplot(data = Cell_type, aes(x = CellType, y = P_major, fill = CellType))+
geom_violin(adjust = .2, show.legend = FALSE)+
ylab("Percentage of major isoform")
ggplot(data = Cell_type, aes(x = CellType, y = P_major2, fill = CellType))+
geom_violin(adjust = 1, show.legend = FALSE)+
ylab("Percentage of major isoform")
dev.off()
Cell_type <- read.table("/mnt/data5/BGI/UCB/ExpMat_NewID/Cell_type.txt", header = F, row.names = 1, sep = "\t", stringsAsFactors=F)
colnames(Cell_type) <- "CellType"
Cell_type <- data.frame(CellType = Cell_type[order(Cell_type),], row.names = row.names(Cell_type)[order(Cell_type)])
Cell_type$Individual <- substr(row.names(Cell_type), 1, 4)
Cell_type$Isoforms <- colSums(cov_tu[,row.names(Cell_type)]>0)
table(Cell_type$CellType, Cell_type$Isoforms)
# 0 1 2 3 4 5
# B Cells 329 207 49 12 10 0
# CD4+ T Cells 684 508 275 97 111 3
# CD8+ T Cells 240 173 93 30 42 1
# Megakaryocytes 11 8 2 1 2 0
# Monocytes 33 13 1 2 3 0
# NK cells 46 31 17 0 5 0
#### Bar chart of cell isoform numbers
Cell_type$Isoforms <- as.factor(Cell_type$Isoforms)
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bar chart of cell isoform numbers.pdf")
ggplot(Cell_type, aes(Isoforms))+
geom_bar(aes(fill = CellType))+
geom_text(data = data.frame(table(Cell_type$Isoforms)), aes(x=Var1,y=Freq, label=Freq), nudge_y=20)
dev.off()
#### Bar chart of cell type isoform numbers
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bar chart of cell type isoform numbers2.pdf")
ggplot(Cell_type, aes(CellType))+
geom_bar(aes(fill = Isoforms))+
geom_text(data = data.frame(table(Cell_type$CellType)), aes(x=Var1,y=Freq, label=Freq), nudge_y=20)
ggplot(Cell_type, aes(CellType))+
geom_bar(aes(fill = Isoforms), position = "fill")+
scale_x_discrete(labels=paste(levels(Cell_type$CellType), "(", table(Cell_type$CellType), ")", sep = ""))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Fraction")
dev.off()
#### Only care about the major isoform
for(i in 1:ncol(cov_tu)){
cov_tu[,i][-which.max(cov_tu[,i])] <- 0
}
cov_tu <- cov_tu[,colSums(cov_tu)!=0]
to_plot <- data.frame(apply(cov_tu,2,function(x) row.names(cov_tu)[x>0]))
colnames(to_plot) <- "CD45_Type"
Cell_type <- read.table("/mnt/data5/BGI/UCB/ExpMat_NewID/Cell_type.txt", header = F, row.names = 1, sep = "\t", stringsAsFactors=F)
colnames(Cell_type) <- "CellType"
Cell_type <- data.frame(CellType = Cell_type[order(Cell_type),], row.names = row.names(Cell_type)[order(Cell_type)])
Cell_type$Individual <- substr(row.names(Cell_type), 1, 4)
plot_tab <- cbind(Cell_type[row.names(to_plot),],to_plot)
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bar chart of cell type major isoform fraction.pdf")
ggplot(plot_tab, aes(CellType))+
geom_bar(aes(fill = CD45_Type), position = "fill")+
scale_x_discrete(labels=paste(levels(plot_tab$CellType), "(", table(plot_tab$CellType), ")", sep = ""))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Fraction")
dev.off()
#save(cov_tu, file = "/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/cov_tu.RData")
load("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/cov_tu.RData")
cov_tu_2 <- cov_tu[,colSums(cov_tu>0)==2]
apply(cov_tu_2, 2, function(x) paste(row.names(cov_tu_2)[x>0], collapse="|"))
table(apply(cov_tu_2, 2, function(x) paste(row.names(cov_tu_2)[x>0], collapse="|")))
# RABC|RBC RABC|RC RABC|RO RAB|RABC RAB|RB RAB|RO RAC|RBC RA|RAB
# 134 1 4 72 36 2 1 1
# RBC|RO RB|RBC RB|RC RB|RO
# 6 160 1 19
test <- table(apply(cov_tu_2, 2, function(x) paste(row.names(cov_tu_2)[x>0], collapse="|")))
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Distribution of isoform_2.pdf")
par(mar=c(10.1, 4.1, 4.1, 4.1))
barplot(sort(test, decreasing=T), las=2, ylab = "Fequency")
dev.off()
to_plot <- data.frame(apply(cov_tu_2, 2, function(x) paste(row.names(cov_tu_2)[x>0], collapse="|")))
colnames(to_plot) <- "CD45_Type"
Cell_type <- read.table("/mnt/data5/BGI/UCB/ExpMat_NewID/Cell_type.txt", header = F, row.names = 1, sep = "\t", stringsAsFactors=F)
colnames(Cell_type) <- "CellType"
Cell_type <- data.frame(CellType = Cell_type[order(Cell_type),], row.names = row.names(Cell_type)[order(Cell_type)])
Cell_type$Individual <- substr(row.names(Cell_type), 1, 4)
plot_tab <- cbind(Cell_type[row.names(to_plot),],to_plot)
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bar chart of cell type major isoform_2 fraction.pdf")
ggplot(plot_tab, aes(CellType))+
geom_bar(aes(fill = CD45_Type), position = "fill")+
scale_x_discrete(labels=paste(levels(plot_tab$CellType), "(", table(plot_tab$CellType), ")", sep = ""))+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
ylab("Fraction")
dev.off()
to_plot <- data.frame(colSums(cov_tu>0))
colnames(to_plot) <- "Isoforms"
plot_tab <- cbind(median_merge[row.names(to_plot),], to_plot)
plot_tab$Isoforms <- factor(plot_tab$Isoforms)
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Box plot of exon expression of different isoform numbers.pdf")
ggplot(plot_tab, aes(x = Isoforms, y = log10(Exon_Sum+1)))+
geom_boxplot()
dev.off()
#### bulk CD45 Sashimi plot
path<-file.path("/mnt/data5/BGI/UCB/xiaqiuqi/result/cell_20_samples_results_files")
multmerge = function(mypath){
#need to change the pattern accordingly !!!
filenames=list.files(path=mypath, full.names=TRUE)
datalist = lapply(paste(filenames,"quant.sf",sep = "/"), function(x){
tmp<-fread(x, header=TRUE, select = 5, sep = "\t")
setnames(tmp, "NumReads",strsplit(x,"/")[[1]][9])
return(tmp)})
Reduce(function(x,y) {cbind(x,y)}, datalist)
}
system.time(bulk_merge<-multmerge(path))
bulk_merge <- as.data.frame(bulk_merge)
row.names(bulk_merge) <- c("RA","RAB","RABC","RAC","RB","RBC","RC","RO")
mon_merge <- bulk_merge[,grep("mono", colnames(bulk_merge))]
tcl_merge <- bulk_merge[,grep("mono", colnames(bulk_merge), invert=T)]
tcl_merge[tcl_merge<50] <- 0
rowSums(tcl_merge>0)
# RA RAB RABC RAC RB RBC RC RO
# 0 10 10 0 9 10 0 2
mon_merge[mon_merge<50] <- 0
rowSums(mon_merge>0)
# RA RAB RABC RAC RB RBC RC RO
# 0 10 10 0 10 10 0 10
tcl_merge <- cbind(rowSums(cov_tu[,row.names(Cell_type)[Cell_type$CellType=="CD4+ T Cells"]]), tcl_merge)
tcl_merge <- apply(tcl_merge, 2, function(x) x/sum(x))
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bar chart of cd4 sc and bulk.pdf")
par(mar=c(10.1, 4.1, 4.1, 8.1))
x <- barplot(tcl_merge, las = 2, col = RColorBrewer::brewer.pal(n = 8, name = "Set3"),
ylab = "%", names.arg = rep(c("SC","Bulk"), c(1,10)))
legend("topright", inset=c(-.2,0), legend = rownames(tcl_merge),
bty = "n", fill = RColorBrewer::brewer.pal(n = 8, name = "Set3"), title = "Isoforms", xpd=TRUE)
dev.off()
mon_merge <- cbind(rowSums(cov_tu[,row.names(Cell_type)[Cell_type$CellType=="Monocytes"]]), mon_merge)
mon_merge <- apply(mon_merge, 2, function(x) x/sum(x))
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bar chart of Monocytes sc and bulk.pdf")
par(mar=c(10.1, 4.1, 4.1, 8.1))
x <- barplot(mon_merge, las = 2, col = RColorBrewer::brewer.pal(n = 8, name = "Set3"),
ylab = "%", names.arg = rep(c("SC","Bulk"), c(1,10)))
legend("topright", inset=c(-.2,0), legend = rownames(mon_merge),
bty = "n", fill = RColorBrewer::brewer.pal(n = 8, name = "Set3"), title = "Isoforms", xpd=TRUE)
dev.off()
list.files("/mnt/data5/BGI/UCB/xiaqiuqi/result/cell_20_samples_results_files")
tcel <- list.files("/mnt/data5/BGI/UCB/xiaqiuqi/result/cell_20_samples_results_files")[grep("mono", list.files("/mnt/data5/BGI/UCB/xiaqiuqi/result/cell_20_samples_results_files"), invert=T)]
mono <- list.files("/mnt/data5/BGI/UCB/xiaqiuqi/result/cell_20_samples_results_files")[grep("mono", list.files("/mnt/data5/BGI/UCB/xiaqiuqi/result/cell_20_samples_results_files"), invert=F)]
tcel <- substr(tcel,1,15)
mono <- substr(mono,1,15)
Tcel_tsv <- read.table("/mnt/nfs_nas/Lab/People/tangchao/Project/IR_QTL/data/phenotype/BAM_tsv/Tcells.tsv", header = F, stringsAsFactors = F, sep = "\t")
Mono_tsv <- read.table("/mnt/nfs_nas/Lab/People/tangchao/Project/IR_QTL/data/phenotype/BAM_tsv/Monocytes.tsv", header = F, stringsAsFactors = F, sep = "\t")
mapply(function(x) grep(x,Mono_tsv$V2), mono)
write.table(Mono_tsv[mapply(function(x) grep(x,Mono_tsv$V2), mono),], file = "/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Monocytes_sashimiplot_bam.tsv", sep = "\t", row.names = F, quote = F, col.names = F)
python /mnt/data5/BGI/UCB/tangchao/soft/ggsashimi/sashimi-plot.py \
-b /mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Monocytes_sashimiplot_bam.tsv \
-c chr1:198660476-198673501 \
-g /mnt/nfs_nas/Lab/People/tangchao/Project/IR_QTL/data/software/ggsashimi/gencode.v15.annotation.transcript.exon.gtf \
-M 11 \
--alpha 0.3 \
--base-size=20 \
--ann-height=12 \
--height=4 \
--width=18 \
-o /mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bulk_Monocytes.pdf \
-C 3
list.files("/mnt/nfs_nas/EGAD00001002671/EGAD00001002671_bam_decrypted/", pattern = "bam$")
tcel_bam <- list.files("/mnt/nfs_nas/EGAD00001002671/EGAD00001002671_bam_decrypted", pattern = "bam$", full.names = T)[grep("mRNA", list.files("/mnt/nfs_nas/EGAD00001002671/EGAD00001002671_bam_decrypted/", pattern = "bam$"))[1:10]]
data.frame(V1 = tcel, V2 = tcel_bam, V3 = "T Cells")
write.table(data.frame(V1 = tcel, V2 = tcel_bam, V3 = "T Cells"), file = "/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Tcells_sashimiplot_bam.tsv", sep = "\t", row.names = F, quote = F, col.names = F)
python /mnt/data5/BGI/UCB/tangchao/soft/ggsashimi/sashimi-plot.py \
-b /mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Tcells_sashimiplot_bam.tsv \
-c chr1:198660476-198673501 \
-g /mnt/nfs_nas/Lab/People/tangchao/Project/IR_QTL/data/software/ggsashimi/gencode.v15.annotation.transcript.exon.gtf \
-M 11 \
--alpha 0.3 \
--base-size=20 \
--ann-height=12 \
--height=4 \
--width=18 \
-o /mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/Bulk_CD4TCellss.pdf \
-C 3
#### CD45 gene expression bias
library(data.table)
tpm <- fread("/mnt/data5/BGI/UCB/ExpMat_NewID/UCB_rsem_TPM_mat.txt", header = T, sep = "\t")
setkey(tpm,V1)
CD45_tpm <- data.frame(tpm["ENSG00000081237",][,-1])
table(colSums(cov_tu>0))
# 0 1 2 3 4 5
# 1343 940 437 142 173 4
cell_iso <- as.data.frame(colSums(cov_tu>0))
colnames(cell_iso) <- "Isoforms"
CD45_tpm[,row.names(cell_iso)]
cell_iso <- cbind(cell_iso, t(CD45_tpm[,row.names(cell_iso)]))
colnames(cell_iso)[2] <- "TPM"
cell_iso$Isoforms <- factor(cell_iso$Isoforms)
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/CD45_gene_expression_of_different_sioforms.pdf")
ggplot(cell_iso, aes(x = Isoforms, y = log10(TPM+1)))+
geom_boxplot()
dev.off()
#### sequence depth bias
sta <- read.table("/mnt/data5/BGI/UCB/ExpMat_NewID/Basic_stat.txt", header = T, sep = "\t", row.names = 1)
cell_iso$Input_reads <- sta[row.names(cell_iso), "Input_reads"]
cell_iso$Mapped_reads <- sta[row.names(cell_iso), "Mapped_reads"]
pdf("/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/sequence_depth_of_different_sioforms.pdf")
ggplot(cell_iso, aes(x = Isoforms, y = log10(Input_reads+1)))+
geom_boxplot()
ggplot(cell_iso, aes(x = Isoforms, y = log10(Mapped_reads+1)))+
geom_boxplot()
dev.off()
#### 5' or 3' bias
test <- fread("/mnt/data5/BGI/UCB/tangchao/BCseq/featurecount_ensembl/UCB3.00575.epCount", skip = 1, header = T, sep = "\t")
setkey(test,Geneid)
test2 <- test["ENSG00000081237",]
gtf <- fread("/mnt/data5/BGI/UCB/tangchao/Homo_sapiens.GRCh38.87.transccript.exon.gtf", header = F, sep = "\t")
grep("ENST00000442510", gtf[[9]])
gtf <- gtf[grep("ENST00000442510", gtf[[9]]),]
gtf <- gtf[-1, 1:7]
test2 <- as.data.frame(test2)
colnames(test2)[7] <- strsplit(colnames(test2)[7], "[/_]")[[1]][10]
sum(test2$Start %in% gtf[[4]] & test2$End %in% gtf[[5]])
[1] 33
test2 <- test2[test2$Start %in% gtf[[4]] & test2$End %in% gtf[[5]], ]
test2 <- test2[order(test2$Start, test2$End), ]
files <- list.files("/mnt/data5/BGI/UCB/tangchao/BCseq/featurecount_ensembl", pattern = "epCount$", full.names=T)
mclapply(files, function(x){
test <- fread(x, skip = 1, header = T, sep = "\t")
setkey(test,Geneid)
test2 <- test["ENSG00000081237",]
test2 <- as.data.frame(test2)
colnames(test2)[7] <- strsplit(colnames(test2)[7], "[/_]")[[1]][10]
test2 <- test2[test2$Start %in% gtf[[4]] & test2$End %in% gtf[[5]], ]
test2 <- test2[order(test2$Start, test2$End), ]
return(test2)
}, mc.cores = 10) -> tang
tang <- list()
for(i in 1:length(files)){
print(i)
test <- fread(files[i], skip = 1, header = T, sep = "\t")
setkey(test,Geneid)
test2 <- test["ENSG00000081237",]
test2 <- as.data.frame(test2)
colnames(test2)[7] <- strsplit(colnames(test2)[7], "[/_]")[[1]][10]
test2 <- test2[test2$Start %in% gtf[[4]] & test2$End %in% gtf[[5]], ]
test2 <- test2[order(test2$Start, test2$End), ]
tang[[i]] <- test2
}
save(tang, file = "/mnt/data5/BGI/UCB/tangchao/CD45/CD45_Stat3/exon_exp_of_CD45.RData")
test <- do.call(rbind,lapply(tang, function(x) as.data.frame(t(x[,7]))))
row.names(test) <- unlist(lapply(tang, function(x) colnames(x)[7]))
colnames(test) <- paste("exon", 1:ncol(test), sep = "")
#for(i in 1:nrow(test)){
# print(i)
# test[i, (test[i,]/sum(test[i,])) < 0.05] <- 0
#}
test[test<10] <- 0
##Mfuzz#########
library(Mfuzz)
library(reshape2)
library(tidyverse)
library(marray)
##### A slight change for plot function#####
cos_fuc <- function (eset, cl, mfrow = c(1, 1), colo, min.mem = 0, time.labels,
time.points, ylim.set = c(0, 0), xlab = "Time", ylab = "Expression changes",
x11 = TRUE, ax.col = "black", bg = "white", col.axis = "black",
col.lab = "black", col.main = "black", col.sub = "black",
col = "black", centre = FALSE, centre.col = "black", centre.lwd = 2,
Xwidth = 5, Xheight = 5, single = FALSE,clu = clu, ...)
{
clusterindex <- cl[[3]]
memship <- cl[[4]]
memship[memship < min.mem] <- -1
colorindex <- integer(dim(exprs(eset))[[1]])
if (missing(colo)) {
colo <- c("#FF0000", "#FF1800", "#FF3000", "#FF4800",
"#FF6000", "#FF7800", "#FF8F00", "#FFA700", "#FFBF00",
"#FFD700", "#FFEF00", "#F7FF00", "#DFFF00", "#C7FF00",
"#AFFF00", "#97FF00", "#80FF00", "#68FF00", "#50FF00",
"#38FF00", "#20FF00", "#08FF00", "#00FF10", "#00FF28",
"#00FF40", "#00FF58", "#00FF70", "#00FF87", "#00FF9F",
"#00FFB7", "#00FFCF", "#00FFE7", "#00FFFF", "#00E7FF",
"#00CFFF", "#00B7FF", "#009FFF", "#0087FF", "#0070FF",
"#0058FF", "#0040FF", "#0028FF", "#0010FF", "#0800FF",
"#2000FF", "#3800FF", "#5000FF", "#6800FF", "#8000FF",
"#9700FF", "#AF00FF", "#C700FF", "#DF00FF", "#F700FF",
"#FF00EF", "#FF00D7", "#FF00BF", "#FF00A7", "#FF008F",
"#FF0078", "#FF0060", "#FF0048", "#FF0030", "#FF0018")
}
else {
if (colo == "fancy") {
fancy.blue <- c(c(255:0), rep(0, length(c(255:0))),
rep(0, length(c(255:150))))
fancy.green <- c(c(0:255), c(255:0), rep(0, length(c(255:150))))