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zzov_fig2C&sfig3A&B&D.Rmd
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---
title: "zzov_12batch"
author: "yuyi"
date: "2021/12/20"
output: html_document
---
```{r}
rm(list = ls())
options(stringsAsFactors = F)
source("D:/datamining_library_ge.R")
source("D:/library_yu.R")
library(DMwR2)
library(openxlsx)
library(ggpubr)
df <- ge.readtable(paste0("./CAC20211125qianlj_ZZOV_plasma_TMT16plex_60min_b1__Proteins.txt"))
df1 <- df[df$Protein.FDR.Confidence..Combined=="High",]
df1 <- df1[!grepl(";",df1$Accession),]
df1 <- df1[!grepl("CON_",df1$Accession),]
df2 <- df1[,grep("Ratio|Grouped...F[1-9]*..126",names(df1))]
# name=str_extract(names(df2),"[F][0-9]*..1[0-9][0-9][A-Z]*")
name=str_extract(names(df2),"F[0-9]*..[0-9A-Z]*")
name= gsub("\\.\\.","_",name)
name= gsub("F","b",name)
names(df2)=name
df1$Gene.Symbol[which(df1$`Gene.Symbol`=="")]=NA
df2 <- as.data.frame(df2)
row.names(df2)=paste0(df1$Accession,"_",as.character(sapply(df1$`Gene.Symbol`, function(x){str_split(x,";")[[1]][1]})))
df3 <- df2[,-grep("126",names(df2))]
#pool
pool<- df2[,grep("126",names(df2))]
NA_threshold_table(df3)
NA_threshold_table(pool)
df3_7 <- df3[apply(df3,1, function(x){sum(is.na(x))/ncol(df3)})<0.7,]
# df5_7[is.na(df5_7)]<-min(df5_7)*0.5
pool_7 <- pool[apply(pool,1, function(x){sum(is.na(x))/ncol(pool)})<0.7,]
# pool_7[is.na(pool_7)]<-min(pool_7)*0.5
write.csv(df5,"matrix.csv")
write.csv(df3_7,"12batch_matrix0.7.csv")
```
```{r}
info <- read_excel("./20211207ZZOV_plasmaValidation_batchDesign.xlsx",sheet = 3)
Input3 <- read_csv("./BatchFree_batch2021-12-20.csv")
Input3=Input3[,-1]
rownames(Input3)= name
library(ggthemes)
set.seed(10)
TMTbatch<- info$TMTbatch[match(colnames(Input1),info$name)]
TMTbatch2<-str_extract(TMTbatch,"[0-9]*[0-9]")
TMTbatch2<-as.numeric(TMTbatch2)
ge.plot.pca2(Input3,TMTbatch2,title="TMT_3",width=12,height=8)
caabatch<- info$matrix[match(colnames(Input1),info$name)]
ge.plot.pca(Input3,caabatch,title="CAA_3",width=12,height=8)
label1<- info$label1[match(colnames(Input1),info$name)]
ge.plot.pca(Input3,label1,title="nbh_3",width=12,height=8)
ge.plot.pca(Input3,label1,title="nbh_3_q",width=12,height=8,quan = T)
library(dplyr)
id.rep<- info$SampleName[match(colnames(Input3),info$name)]
rep<-Input3
colnames(rep)<-id.rep
repB <- names(rep)[grepl("_rep", names(rep))]
type<-gsub('_rep', '', repB)
############################rep cv
# fivenum(tmp)
sum.cv <-c()
mean_value <- rownames(rep)
mid.cv <-c()
type<-c()
# i=repB[1]
for (i in repB) {
repa <- str_split(i,"_rep")[[1]][1]
tmp <- rep[,which(names(rep) %in% c(repa,i))]
if (length(tmp)==2){
type<-c(type,repa)
tmp.cv <- apply(tmp, 1 , function(x){sd(x,na.rm = T)/mean(x,na.rm =T)})
tmp.mean <- apply(tmp, 1 , mean,na.rm=T)
sum.cv <- c(sum.cv,tmp.cv)
mean_value <-cbind.data.frame(mean_value,tmp.mean)
mid.tmp <- median(tmp.cv,na.rm = T)
mid.cv <- c(mid.cv,mid.tmp)
}else{return}
}
mean_value <- mean_value[,-1]
names(mean_value) <- repB
df.cv <- data.frame(cv=sum.cv,sample=rep(type,each=nrow(rep)))
# df.cv <- data.frame(cv=sum.cv,sample=rep(info$interORintra[match(repB,info$SampleName)],each=nrow(rep)))
# P<-df.cv[df.cv$sample=="5_38N",]
# write.csv(P,"5_38N.csv")
df.cv <- df.cv[!is.na(df.cv$cv),]
#MEDIAN
median.cv <- data.frame(level=mid.cv,sample=repB)
write.csv(median.cv,file = "CV.csv")
n_fun <- function(x){
return(data.frame(y = max(x)+0.1, label = paste0("median =",round(median(x),3))))
}
#plot violin
p<-ggplot(df.cv, aes(x = sample, y=cv,color=sample)) +
geom_violin(trim=FALSE)+
scale_fill_manual(values=brewer.pal(12,"Set3")[c(1:10)])+
geom_boxplot(width=0.1)+
theme(legend.direction = 'horizontal',legend.position = 'top',panel.grid.major =element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(),axis.line = element_line(colour = "black"))+
theme(axis.text = element_text(size = 16,colour = "black"),text = element_text(size = 30,colour = "black"))+
theme(axis.text.x = element_text( hjust = 1,angle = 45))+
theme(legend.position = "none")+
stat_summary(fun.data =n_fun, geom="text")
ggsave("CV.pdf",plot =p ,device = NULL,width = 8, height = 8)
df.cv <- data.frame(cv=sum.cv,sample=rep("rep",each=nrow(rep)))
# df.cv <- data.frame(cv=sum.cv,sample=rep(info$interORintra[match(repB,info$SampleName)],each=nrow(rep)))
# P<-df.cv[df.cv$sample=="5_38N",]
# write.csv(P,"5_38N.csv")
#DEL NA
df.cv <- df.cv[!is.na(df.cv$cv),]
#median
# median.cv <- data.frame(level=mid.cv,sample=repB)
# write.csv(median.cv,file = "CV.csv")
n_fun <- function(x){
return(data.frame(y = max(x)+0.1, label = paste0("median =",round(median(x),3))))
}
#plot violin
p<-ggplot(df.cv, aes(x = sample, y=cv,color=sample)) +
geom_violin(trim=FALSE)+
scale_fill_manual(values=brewer.pal(12,"Set3")[c(1:10)])+
geom_boxplot(width=0.1)+
theme(legend.direction = 'horizontal',legend.position = 'top',panel.grid.major =element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(),axis.line = element_line(colour = "black"))+
theme(axis.text = element_text(size = 16,colour = "black"),text = element_text(size = 30,colour = "black"))+
theme(axis.text.x = element_text( hjust = 1,angle = 45))+
theme(legend.position = "none")+
stat_summary(fun.data =n_fun, geom="text")
ggsave("CV_all.pdf",plot =p ,device = NULL,width = 8, height = 8)
library(dplyr)
id.rep<- info$SampleName[match(colnames(Input3),info$name)]
rep<-Input3
colnames(rep)<-id.rep
repB <- names(rep)[grepl("_rep", names(rep))]
type<-gsub('_rep', '', repB)
############################
sum.cv <- matrix(NA, nrow(Input3), 1)
mid.cv <-c()
type<-c()
# i=repB[1]
for (i in repB) {
repa <- str_split(i,"_rep")[[1]][1]
tmp <- rep[,which(names(rep) %in% c(repa,i))]
if (length(tmp)==2){
type<-c(type,repa)
tmp.cv <- apply(tmp, 1,function(x){mean(x,na.rm = TRUE)})
sum.cv <- cbind.data.frame(sum.cv,tmp.cv)
}else{return}
}
sum.cv <- sum.cv[,-1]
names(sum.cv) <- type
sum.cv[is.na(sum.cv)]<-NA
df2<-Input3
names(df2)<-id.rep
tmp1 <- df2[,-which(names(df2) %in% c(type,repB))]
df2 <- cbind.data.frame(sum.cv,tmp1)
rownames(df2)<-rownames(Input3)
# nad2<-gsub('c', '', names(df2))
# names(df2)<-nad2
write.csv(df2,file = "combinerepInput3.csv")
# yu.cv2(df2,title = "combinerepInput3",log2 = F)
df2<-read.csv("./combinerepInput3.csv",row.names = 1 )
# name<-df2$X1
# df2<-df2[,-1]
# rownames(df2)<-name
linda.volcano.plot <- function(data,#data.frame
group1,
group2,
label1,
label2,
paired = F,
pvalue= 0.05,
P.adjust=T,
var.equal = F,
fold_change = 2,
up_col="#EC9B8F", #dw_col = "#9370DB",up_col = "#FF7256"
dw_col="#A7DEE8"){
df1 <- data
#df1[is.na(df1)]=min(df1,na.rm = T)*0.8
#foldchange
df1$`log2(foldchange)` <- apply(df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
if(paired){
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = paired,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}else{
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = F,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}
#add p-value
df1$P_value<- P_value
df1$P_value_adjust<-p.adjust(df1$P_value, method="BH")
if(P.adjust){
#plot
pdf(paste0(label1,"_",label2, "_volcano.pdf"),width=4, height=4)
plot(df1$`log2(foldchange)`, -log10(df1$P_value_adjust), col="#00000033", pch=19,cex=0.5,
xlab=paste("log2 (fold change)"),
ylab="-log10 (adjust P value)",
xlim= c(-round(max(abs(df1$`log2(foldchange)`),na.rm=T),1),round(max(abs(df1$`log2(foldchange)`),na.rm=T),1)),
main=paste0(label1," : ",label2))
#save up and down protein
up <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- c(row.names(up),row.names(down))
points(up$`log2(foldchange)`, -log10(up$P_value_adjust), col=1,
#bg = brewer.pal(9, "YlOrRd")[6],
bg=up_col,
pch=21, cex=0.5)
points(down$`log2(foldchange)`, -log10(down$P_value_adjust), col = 1,
#bg = brewer.pal(11,"RdBu")[9],
bg=dw_col,
pch = 21,cex=0.5)
abline(h=-log10(pvalue), v=c(-log2(fold_change),log2(fold_change)), lty=2,lwd=1)
h1<-df1[match(heat$elements,rownames(df1)),]
text(h1$`log2(foldchange)`, -log10(h1$P_value_adjust),rownames(h1),cex=0.2)
dev.off()}else{
pdf(paste0(label1,"_",label2, "_volcano.pdf"),width=4, height=4)
plot(df1$`log2(foldchange)`, -log10(df1$P_value), col="#00000033", pch=19,
xlab=paste("log2 (fold change)"),
ylab="-log10 (P value)",xlim=c(-round(max(abs(df1$`log2(foldchange)`),na.rm=T),1),round(max(abs(df1$`log2(foldchange)`),na.rm=T),1)),
main=paste0(label1," : ",label2))
#
up <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- c(row.names(up),row.names(down))
points(up$`log2(foldchange)`, -log10(up$P_value), col=1,
# bg = brewer.pal(9, "YlOrRd")[6],
bg=up_col,
pch=21, cex=1.2)
points(down$`log2(foldchange)`, -log10(down$P_value), col = 1,
# bg = brewer.pal(11,"RdBu")[9],
bg=dw_col,
pch = 21,cex=1.2)
abline(h=-log10(pvalue), v=c(-log2(fold_change),log2(fold_change)), lty=2,lwd=1)
dev.off()
}
return(differprot)
}
linda.volcano.plot(df2,two,HS,label1 = "NM+BN",label2 = "HS-1",P.adjust = T,fold_change = 1.2)
group<- info$label1[match(colnames(df2),info$SampleName)]
HS <- which(group=="HS")
NM <- which(group=="NM")
BN <- which(group=="BN")
two<-which(group=="BN"|group=="NM")
df3<-cbind(df2[,NM],df2[,BN],df2[,HS])
group<-info$label1[match(colnames(df3),info$SampleName)]
t.test(df2[HS,1],df2[NM,1],paired = F,var.equal = F)$p.value
t.test(df2[BN,1],df2[NM,1],paired = F,var.equal = F)$p.value
group[which(group=="BN"|group=="NM")]<-"NM+BN"
heat <- read_excel("20211227NM_BN_HS_boxplot_list.xlsx",sheet = 1)
h1<-match(heat$elements,rownames(df3))
for (i in 1:8) {
data <- data.frame(value=as.numeric(df3[h1[i],]),group=as.factor(group))
ge.plot.boxplot(data,data$group,data$value,data$group,paste0(row.names(df3)[h1[i]]),title=paste0(row.names(df3)[h1[i]],"_boxplot"))
}
```