WGCNA与免疫浸润相关分析

rm(list=ls())
library("WGCNA")            #引用WGCNA包
library("limma")            #引用limma包
expFile="uniq.symbol.txt"     #输入文件名字
# normalCount=49              #正常样品数目
# tumorCount=502              #肿瘤样品数目


#读取文件,并对输入文件整理
rt=read.table(expFile,sep="\t",header=T,check.names=F)
rt=as.matrix(rt)
rownames(rt)=rt[,1]
exp=rt[,2:ncol(rt)]
dimnames=list(rownames(exp),colnames(exp))
data=matrix(as.numeric(as.matrix(exp)),nrow=nrow(exp),dimnames=dimnames)
data=avereps(data)
data=log2(data+1)
data=data[apply(data,1,sd)>0.5,]
datExpr0=t(data)
group_1=sapply(strsplit(rownames(datExpr0),"\\-"),"[",4)
group_2=sapply(strsplit(group_1,""),"[",1)
group_2=gsub("2","1",group_2)
group_2=as.data.frame(group_2)
datExpr0=cbind(group_2,datExpr0)
datExpr0 <- datExpr0[which(datExpr0$group_2 %in% 0),]
dim(datExpr0)[2]
datExpr0 <- datExpr0[,-1]

###检查缺失值
gsg = goodSamplesGenes(datExpr0, verbose = 3)
if (!gsg$allOK)
{
  # Optionally, print the gene and sample names that were removed:
  if (sum(!gsg$goodGenes)>0)
    printFlush(paste("Removing genes:", paste(names(datExpr0)[!gsg$goodGenes], collapse = ", ")))
  if (sum(!gsg$goodSamples)>0)
    printFlush(paste("Removing samples:", paste(rownames(datExpr0)[!gsg$goodSamples], collapse = ", ")))
  # Remove the offending genes and samples from the data:
  datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes]
}


# 
# #读取文件,并对输入文件整理
# exp=read.table(expFile,sep="\t",header=T,row.names = 1,check.names = F)
# exp=avereps(exp)
# data=exp
# data[1:4,1:4]
# data=log2(data+1)
# data=data[apply(data,1,sd)>0.5,]
# datExpr0=t(data)
# group_1=sapply(strsplit(rownames(datExpr0),"\\-"),"[",4)
# group_2=sapply(strsplit(group_1,""),"[",1)
# group_2=gsub("2","1",group_2)
# group_2=as.data.frame(group_2)
# datExpr0=cbind(group_2,datExpr0)
# datExpr0=datExpr0[which(datExpr0$group_2==0),-1]
# rownames(datExpr0)=gsub("(.*?)\\-(.*?)\\-(.*?)\\-(.*?)\\-.*",
#                         "\\1\\-\\2\\-\\3",rownames(datExpr0))
# 
# ###检查缺失值
# gsg = goodSamplesGenes(datExpr0, verbose = 3)
# if (!gsg$allOK)
# {
#   # Optionally, print the gene and sample names that were removed:
#   if (sum(!gsg$goodGenes)>0)
#     printFlush(paste("Removing genes:", paste(names(datExpr0)[!gsg$goodGenes], collapse = ", ")))
#   if (sum(!gsg$goodSamples)>0)
#     printFlush(paste("Removing samples:", paste(rownames(datExpr0)[!gsg$goodSamples], collapse = ", ")))
#   # Remove the offending genes and samples from the data:
#   datExpr0 = datExpr0[gsg$goodSamples, gsg$goodGenes]
# }

###样品聚类
sampleTree = hclust(dist(datExpr0), method = "average")
pdf(file = "1_sample_cluster.pdf", width = 12, height = 9)
par(cex = 0.6)
par(mar = c(0,4,2,0))
plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5, cex.axis = 1.5, cex.main = 2)
###剪切线
abline(h = 19000, col = "red")
dev.off()

###删除剪切线以下的样品
clust = cutreeStatic(sampleTree, cutHeight = 19000, minSize = 10)
table(clust)
keepSamples = (clust==1)
datExpr0 = datExpr0[keepSamples, ]


###准备临床数据
clinical=read.table(file = "CIBERSORT-Results.txt",sep="\t",header = T,row.names = 1,check.names = F)
clinical=na.omit(clinical)
group_1=sapply(strsplit(rownames(clinical),"\\-"),"[",4)
group_2=sapply(strsplit(group_1,""),"[",1)
group_2=gsub("2","1",group_2)
group_2=as.data.frame(group_2)
clinical=cbind(group_2,clinical)
clinical <- clinical[which(clinical$group_2 %in% 0),]
dim(clinical)[2]
clinical <- clinical[,-c(1,24,25,26)]
flag <- apply(clinical,2,function(x) 
  sum(x == 0) < dim(clinical)[1]*4/5)
clinical<- clinical[,which(flag)] 

sample=as.character(rownames(datExpr0))
traitsRows=match(sample,rownames(clinical))
clinical=clinical[traitsRows,]


###样品聚类
sampleTree2 = hclust(dist(datExpr0), method = "average")
traitColors = numbers2colors(clinical, signed = FALSE)
pdf(file="2_sample_heatmap.pdf",width=15,height=12)
plotDendroAndColors(sampleTree2, traitColors,
                    groupLabels = names(clinical),
                    main = "Sample dendrogram and trait heatmap")
dev.off()

###power值散点图
enableWGCNAThreads()   #多线程工作
powers = c(1:20)       #幂指数范围1:20
sft = pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5)
pdf(file="3_scale_independence.pdf",width=9,height=5)
par(mfrow = c(1,2))
cex1 = 0.9
###拟合指数与power值散点图
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
     main = paste("Scale independence"));
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
     labels=powers,cex=cex1,col="red");
abline(h=0.90,col="red") #可以修改
###平均连通性与power值散点图
plot(sft$fitIndices[,1], sft$fitIndices[,5],
     xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
     main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
dev.off()


###邻接矩阵转换
sft #查看最佳power值
softPower =sft$powerEstimate #最佳power值
softPower
adjacency = adjacency(datExpr0, power = softPower)

###TOM矩阵
TOM = TOMsimilarity(adjacency);
dissTOM = 1-TOM

###基因聚类
geneTree = hclust(as.dist(dissTOM), method = "average");
pdf(file="4_gene_clustering.pdf",width=12,height=9)
plot(geneTree, xlab="", sub="", main = "Gene clustering on TOM-based dissimilarity",
     labels = FALSE, hang = 0.04)
dev.off()


###动态剪切模块识别
minModuleSize = 50      #模块基因数目
dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM,
                            deepSplit = 2, pamRespectsDendro = FALSE,
                            minClusterSize = minModuleSize);
table(dynamicMods)
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)
pdf(file="5_Dynamic_Tree.pdf",width=8,height=6)
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut",
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05,
                    main = "Gene dendrogram and module colors")
dev.off()


###相似模块聚类
MEList = moduleEigengenes(datExpr0, colors = dynamicColors)
MEs = MEList$eigengenes
MEDiss = 1-cor(MEs);
METree = hclust(as.dist(MEDiss), method = "average")
pdf(file="6_Clustering_module.pdf",width=7,height=6)
plot(METree, main = "Clustering of module eigengenes",
     xlab = "", sub = "")
MEDissThres = 0.25 #剪切高度可修改
abline(h=MEDissThres, col = "red")
dev.off()


###相似模块合并
merge = mergeCloseModules(datExpr0, dynamicColors, cutHeight = MEDissThres, verbose = 3)
mergedColors = merge$colors
mergedMEs = merge$newMEs
pdf(file="7_merged_dynamic.pdf", width = 9, height = 6)
plotDendroAndColors(geneTree, mergedColors,"Dynamic Tree Cut",
                    dendroLabels = FALSE, hang = 0.03,
                    addGuide = TRUE, guideHang = 0.05,
                    main = "Gene dendrogram and module colors(TCGA)")
dev.off()
moduleColors = mergedColors
table(moduleColors)
colorOrder = c("grey", standardColors(50))
moduleLabels = match(moduleColors, colorOrder)-1
MEs = mergedMEs

a=as.matrix(table(moduleColors))
pdf("barplot.pdf",width = 21,height = 8)
barplot(table(moduleColors),col = rownames(a))
dev.off()
###模块与性状数据热图
datTraits=clinical
nGenes = ncol(datExpr0)
nSamples = nrow(datExpr0)
moduleTraitCor = cor(MEs, datTraits, use = "p")
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples)
pdf(file="8_Module_trait.pdf",width=11,height=20)
textMatrix = paste(signif(moduleTraitCor, 2), "\n(",
                   signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
par(mar = c(10, 10, 3, 3))
labeledHeatmap(Matrix = moduleTraitCor,
               xLabels = names(datTraits),
               yLabels = names(MEs),
               ySymbols = names(MEs),
               colorLabels = FALSE,
               colors = blueWhiteRed(50),
               textMatrix = textMatrix,
               setStdMargins = FALSE,
               cex.text = 0.5,
               zlim = c(-1,1),
               main = paste("Module-trait relationships(TCGA)"))
dev.off()


###计算MM和GS值
modNames = substring(names(MEs), 3)
geneModuleMembership = as.data.frame(cor(datExpr0, MEs, use = "p"))
MMPvalue = as.data.frame(corPvalueStudent(as.matrix(geneModuleMembership), nSamples))
names(geneModuleMembership) = paste("MM", modNames, sep="")
names(MMPvalue) = paste("p.MM", modNames, sep="")
traitNames=names(datTraits)
geneTraitSignificance = as.data.frame(cor(datExpr0, datTraits, use = "p"))
GSPvalue = as.data.frame(corPvalueStudent(as.matrix(geneTraitSignificance), nSamples))
names(geneTraitSignificance) = paste("GS.", traitNames, sep="")
names(GSPvalue) = paste("p.GS.", traitNames, sep="")


###批量输出性状和模块散点图
for (trait in traitNames){
  traitColumn=match(trait,traitNames)  
  for (module in modNames){
    column = match(module, modNames)
    moduleGenes = moduleColors==module
    if (nrow(geneModuleMembership[moduleGenes,]) > 1){
      outPdf=paste("9_", trait, "_", module,".pdf",sep="")
      pdf(file=outPdf,width=7,height=7)
      par(mfrow = c(1,1))
      verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]),
                         abs(geneTraitSignificance[moduleGenes, traitColumn]),
                         xlab = paste("Module Membership in", module, "module"),
                         ylab = paste("Gene significance for ",trait),
                         main = paste("Module membership vs. gene significance\n"),
                         cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)
      abline(v=0.8,h=0.5,col="red")
      dev.off()
    }
  }
}


###输出GS_MM数据
probes = colnames(datExpr0)
geneInfo0 = data.frame(probes= probes,
                       moduleColor = moduleColors)
for (Tra in 1:ncol(geneTraitSignificance))
{
  oldNames = names(geneInfo0)
  geneInfo0 = data.frame(geneInfo0, geneTraitSignificance[,Tra],
                         GSPvalue[, Tra])
  names(geneInfo0) = c(oldNames,names(geneTraitSignificance)[Tra],
                       names(GSPvalue)[Tra])
}

for (mod in 1:ncol(geneModuleMembership))
{
  oldNames = names(geneInfo0)
  geneInfo0 = data.frame(geneInfo0, geneModuleMembership[,mod],
                         MMPvalue[, mod])
  names(geneInfo0) = c(oldNames,names(geneModuleMembership)[mod],
                       names(MMPvalue)[mod])
}
geneOrder =order(geneInfo0$moduleColor)
geneInfo = geneInfo0[geneOrder, ]
write.table(geneInfo, file = "GS_MM.xls",sep="\t",row.names=F)


###输出每个模块的基因
for (mod in 1:nrow(table(moduleColors)))
{  
  modules = names(table(moduleColors))[mod]
  probes = colnames(datExpr0)
  inModule = (moduleColors == modules)
  modGenes = probes[inModule]
  write.table(modGenes, file =paste0("TCGA_",modules,".txt"),sep="\t",row.names=F,col.names=F,quote=F)
}