setwd("C:\\Users\\biowolf\\Desktop\\WGCNA\\WGCNA") library("WGCNA") rt=read.table("input.txt",sep="\t",header=T,check.names=F) dim(rt) datExpr0 = as.data.frame(t(rt[,-1])) names(datExpr0) = rt[,1] rownames(datExpr0) = names(rt[,-1]) gsg = goodSamplesGenes(datExpr0, verbose = 3) gsg$allOK meanFPKM=0.5 n=nrow(datExpr0) datExpr0[n+1,]=apply(datExpr0[c(1:nrow(datExpr0)),],2,mean) datExpr0=datExpr0[1:n,datExpr0[n+1,] > meanFPKM] filtered_fpkm=t(datExpr0) filtered_fpkm=data.frame(rownames(filtered_fpkm),filtered_fpkm) names(filtered_fpkm)[1]="sample" head(filtered_fpkm) sampleTree = hclust(dist(datExpr0), method = "average") pdf(file = "1_sample_cluster.pdf", width = 12, height = 9) par(cex = 0.6) par(mar = c(0,4,2,0)) plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5, cex.axis = 1.5, cex.main = 2) abline(h = 10000, col = "red") dev.off() traitData = read.table("type.txt",row.names=1,header=T,comment.char = "",check.names=F) dim(traitData) names(traitData) fpkmSamples = rownames(datExpr0) traitSamples =rownames(traitData) traitRows = match(fpkmSamples, traitSamples) datTraits = traitData[traitRows,] rownames(datTraits) collectGarbage() sampleTree2 = hclust(dist(datExpr0), method = "average") traitColors = numbers2colors(datTraits, signed = FALSE) pdf(file="2_sample_heatmap.pdf",width=12,height=12) plotDendroAndColors(sampleTree2, traitColors, groupLabels = names(datTraits), main = "Sample dendrogram and trait heatmap") dev.off() enableWGCNAThreads() powers = c(1:20) sft = pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5) pdf(file="3_scale_independence.pdf",width=9,height=5) par(mfrow = c(1,2)) cex1 = 0.9 plot(sft$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$fitIndices[,2], labels=powers,cex=cex1,col="red"); abline(h=0.90,col="red") plot(sft$fitIndices[,1], sft$fitIndices[,5], xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n", main = paste("Mean connectivity")) text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red") dev.off() sft softPower =sft$powerEstimate adjacency = adjacency(datExpr0, power = softPower) TOM = TOMsimilarity(adjacency); dissTOM = 1-TOM 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() minModuleSize = 30 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() MEList = moduleEigengenes(datExpr0, colors = dynamicColors) MEs = MEList$eigengenes MEDiss = 1-cor(MEs); METree = hclust(as.dist(MEDiss), method = "average") pdf(file="6_Clustering_module.pdf",width=7,height=6) plot(METree, main = "Clustering of module eigengenes", xlab = "", sub = "") MEDissThres = 0.25 abline(h=MEDissThres, col = "red") dev.off() merge = mergeCloseModules(datExpr0, dynamicColors, cutHeight = MEDissThres, verbose = 3) mergedColors = merge$colors mergedMEs = merge$newMEs pdf(file="7_merged_dynamic.pdf", width = 9, height = 6) plotDendroAndColors(geneTree, cbind(dynamicColors, mergedColors), c("Dynamic Tree Cut", "Merged dynamic"), dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05) dev.off() moduleColors = mergedColors table(moduleColors) colorOrder = c("grey", standardColors(50)) moduleLabels = match(moduleColors, colorOrder)-1 MEs = mergedMEs nGenes = ncol(datExpr0) nSamples = nrow(datExpr0) moduleTraitCor = cor(MEs, datTraits, use = "p") moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples) pdf(file="8_Module_trait.pdf",width=10,height=6) textMatrix = paste(signif(moduleTraitCor, 2), "\n(", signif(moduleTraitPvalue, 1), ")", sep = "") dim(textMatrix) = dim(moduleTraitCor) par(mar = c(5, 10, 3, 3)) labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = names(MEs), ySymbols = names(MEs), colorLabels = FALSE, colors = greenWhiteRed(50), textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.5, zlim = c(-1,1), main = paste("Module-trait relationships")) dev.off() 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="") picDir="module_trait" dir.create(picDir) for (trait in traitNames){ traitColumn=match(trait,traitNames) for (module in modNames){ column = match(module, modNames) moduleGenes = moduleColors==module if (nrow(geneModuleMembership[moduleGenes,]) > 1){ pdfFile=paste("9_", trait, "_", module,".pdf",sep="") outPdf=paste(picDir,pdfFile,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) dev.off() } } } names(datExpr0) probes = names(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)