rm(list = ls())
library(RColorBrewer)
library(circlize)
library(gplots)
library(viridis)
library(oompaBase)
library(tidyverse)
Sys.setenv(LANGUAGE = "en")  
options(stringsAsFactors = FALSE)  

standarize.fun <- function(indata=NULL, halfwidth=NULL, centerFlag=T, scaleFlag=T) {  
  outdata=t(scale(t(indata), center=centerFlag, scale=scaleFlag))
  if (!is.null(halfwidth)) {
    outdata[outdata>halfwidth]=halfwidth
    outdata[outdata<(-halfwidth)]= -halfwidth
  }
  return(outdata)
}
load("merge.RDATA")
dataa=outTab
colnames(dataa)=str_replace_all(colnames(dataa),"TCGA_","")
colnames(dataa)=str_replace_all(colnames(dataa),"GSE17538_","")
colnames(dataa)=str_replace_all(colnames(dataa),"GSE39582_","")
aaa=read.csv("Cytokine.csv",header = T,sep = ",")
genename1=intersect(aaa$ID,rownames(dataa))
dataa=dataa[genename1,]
aaa=dplyr::filter(aaa,ID %in% genename1)


risk=read.table("score.group.txt",header = T,sep = "\t",row.names = 1)
sameid=intersect(colnames(dataa),rownames(risk))
dataa=dataa[,sameid]
risk=risk[sameid,]
hmdat=as.data.frame(t(dataa))
 
type=data.frame(Type =aaa$Type)
row.names(type)=aaa$ID

Typeid <- type$Type

 
annCol <- data.frame(score = scale(risk$score),
                     RiskType = risk$group,
                     row.names = rownames(risk),
                     stringsAsFactors = F)



 
bbb=annCol[rownames(hmdat),]
sampleType=bbb$RiskType
sigVec=c()
for(i in colnames(hmdat)){
  test=wilcox.test(hmdat[,i] ~ sampleType)
  pvalue=test$p.value
  Sig=ifelse(pvalue<0.001,"***",ifelse(pvalue<0.01,"**",ifelse(pvalue<0.05,"*","")))
  sigVec=c(sigVec, paste0(i, Sig))
}

colnames(hmdat)=sigVec
 

 
library(pheatmap)

 
Type.col <- brewer.pal(n = length(unique(Typeid)),name = "Paired")


 
annRow <- data.frame(Type = factor(Typeid,levels = unique(Typeid)),
                     row.names = colnames(hmdat),
                     stringsAsFactors = F)


unique(annRow$Type)
 
annColors <- list(Type = c("Chemokines and receptors" = Type.col[1], 
                              "Interieukines and receptors" = Type.col[2],
                              "Interferons and receptors" = Type.col[3],
                              "Other cytokines" = Type.col[4]),
                   
                  "score" = greenred(64), 
                  "RiskType" = c("High" = "red","Low" = "blue"))



 
samorder <- rownames(risk[order(risk$score),])



indata <- t(hmdat)
indata <- indata[,colSums(indata) > 0]  
plotdata <- standarize.fun(indata,halfwidth = 2)

 
pheatmap::pheatmap(mat = as.matrix(plotdata[,samorder]),  
                   border_color = NA, 
                   color = bluered(64),  
                   cluster_rows = F,  
                   cluster_cols = F,  
                   show_rownames = T,  
                   show_colnames = F,  
                   annotation_col = annCol[samorder,,drop = F],  
                   annotation_row = annRow,  
                   annotation_colors = annColors,  
                   gaps_col = table(annCol$RiskType)[2],  
                   gaps_row = cumsum(table(annRow$Type)),  
                   cellwidth = 0.8,  
                   cellheight = 10,  
                   filename = "immune heatmap by pheatmap_score.pdf")

ccc=hmdat[rownames(annCol)[annCol$RiskType=="High"],]
ddd=hmdat[rownames(annCol)[annCol$RiskType=="Low"],]
ccc1=colMeans(ccc)
ddd1=colMeans(ddd)
mewdata=data.frame(High=ccc1,Low=ddd1)

plotdata1 <- mewdata
annCol1=data.frame(RiskType=c("High","Low"))
row.names(annCol1)=c("High","Low")

pheatmap::pheatmap(mat = as.matrix(mewdata[,row.names(annCol1)]), 
                   border_color = "grey", 
                   color = bluered(64),  
                   cluster_rows = F,  
                   cluster_cols = F,  
                   show_rownames = T,  
                   show_colnames = F,  
                   annotation_col = annCol1,  
                   annotation_row = annRow,  
                   annotation_colors = annColors,  
                   gaps_row = cumsum(table(annRow$Type)),  
                   cellwidth = 20,  
                   cellheight = 10,  
                   filename = "immune heatmap by pheatmap_score_mean.pdf")