#if (!requireNamespace("BiocManager", quietly = TRUE))
#    install.packages("BiocManager")
#BiocManager::install("GO.db")
#BiocManager::install("preprocessCore")
#BiocManager::install("impute")
#install.packages(c("matrixStats", "Hmisc", "splines", "foreach", "doParallel", "reshape", "fastcluster", "dynamicTreeCut", "survival"))
#install.packages("WGCNA")

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

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]
}

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)
###write.table(filtered_fpkm, file="rt_filter.xls",row.names=F, col.names=T,quote=FALSE,sep="\t")

###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)


###abline(h = 10000, col = "red")
dev.off()

###clust = cutreeStatic(sampleTree, cutHeight = 10000, minSize = 10)
###table(clust)
###keepSamples = (clust==1)
###datExpr0 = datExpr0[keepSamples, ]

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()

###power
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)

nGenes = ncol(datExpr0)
nSamples = nrow(datExpr0)
plotTOM = dissTOM^7
diag(plotTOM) = NA
pdf(file="10_allgene_heatmap.pdf",width=9, height=9)
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")
dev.off()

nSelect = 400
set.seed(10)
select = sample(nGenes, size = nSelect)
selectTOM = dissTOM[select, select]
selectTree = hclust(as.dist(selectTOM), method = "average")
selectColors = moduleColors[select]
plotDiss = selectTOM^7
diag(plotDiss) = NA
pdf(file="11_selectgene_heatmap.pdf",width=9, height=9)
TOMplot(plotDiss, selectTree, selectColors, main = "Network heatmap plot, selected genes")
dev.off()

pdf(file="12_module_dendrogram.pdf",width=6, height=6)
par(cex = 1.0)
plotEigengeneNetworks(MEs, "Eigengene dendrogram", marDendro = c(0,4,2,0), plotHeatmaps = FALSE)
dev.off()

pdf(file="13_module_heatmap.pdf",width=6, height=6)
par(cex = 1.0)
plotEigengeneNetworks(MEs, "Eigengene adjacency heatmap", marHeatmap = c(3,4,2,2), plotDendrograms = FALSE, xLabelsAngle = 90)
dev.off()

pdf(file="14_dendrogram_heatmap.pdf", width=5, height=7.5)
par(cex = 0.9)
plotEigengeneNetworks(MEs, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2), cex.lab = 0.8, xLabelsAngle= 90)
dev.off()


cytoDir="CytoscapeInput"
dir.create(cytoDir)
for (mod in 1:nrow(table(moduleColors)))
{  
  modules = names(table(moduleColors))[mod]  
  probes = names(datExpr0)
  inModule = (moduleColors == modules)
  modProbes = probes[inModule]
  modGenes = modProbes  
  modTOM = TOM[inModule, inModule] 
  dimnames(modTOM) = list(modProbes, modProbes)
  edges_File = paste("CytoscapeInput-edges-", modules , ".txt", sep="")
  nodes_File = paste("CytoscapeInput-nodes-", modules, ".txt", sep="")
  outEdge=paste(cytoDir,edges_File,sep="\\")
  outNode=paste(cytoDir,nodes_File,sep="\\")
  cyt = exportNetworkToCytoscape(modTOM,
                                 edgeFile = outEdge,
                                 nodeFile = outNode,
                                 weighted = TRUE,
                                 threshold = 0.02,
                                 nodeNames = modProbes,
                                 altNodeNames = modGenes,
                                 nodeAttr = moduleColors[inModule])
}