source("http://bioconductor.org/biocLite.R")
biocLite(c("GO.db", "preprocessCore", "impute"))
install.packages(c("matrixStats", "Hmisc", "splines", "foreach", "doParallel", "reshape", "fastcluster", "dynamicTreeCut", "survival"))
install.packages("WGCNA")
install.packages("scatterplot3d")


library(WGCNA)
setwd("C:\\Users\\scikuangren\\Desktop\\WGCNA-R")  

inputdata1="GEO.txt" 
data0=read.table(inputdata1,sep="\t",row.names=1,header=T,check.names=F,quote="!")
datSummary=rownames(data0)
datExpr = t(data0)
no.samples = dim(datExpr)[[1]]
dim(datExpr)


powers1=c(seq(1,10,by=1),seq(12,20,by=2))
RpowerTable=pickSoftThreshold(datExpr, powerVector=powers1)[[2]]
cex1=1
par(mfrow=c(1,2))
pdf("beta.pdf")
plot(RpowerTable[,1], -sign(RpowerTable[,3])*RpowerTable[,2],xlab="
Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n")
text(RpowerTable[,1], -sign(RpowerTable[,3])*RpowerTable[,2], labels=powers1,cex=cex1,col="red")
abline(h=0.85,col="red")
plot(RpowerTable[,1], RpowerTable[,5],xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n")
text(RpowerTable[,1], RpowerTable[,5], labels=powers1, cex=cex1,col="red")
dev.off()


beta1=6 
Connectivity=softConnectivity(datExpr,power=beta1)
pdf("scalefree.pdf",15,10)
par(mfrow=c(1,1))
scaleFreePlot(Connectivity, main=paste("soft threshold, power=",beta1), truncated=T)
dev.off()



ConnectivityCut = 1000 
ConnectivityRank = rank(-Connectivity)	
restConnectivity = ConnectivityRank <= ConnectivityCut
ADJrest = adjacency(datExpr[,restConnectivity], power=beta1)
dissTOM=TOMdist(ADJrest)
hierTOM = hclust(as.dist(dissTOM),method="average")
colorh1= cutreeStaticColor(hierTOM,cutHeight = 0.8, minSize = 3) 
pdf("module.pdf")
par(mfrow=c(2,1),mar=c(2,4,1,1))
plot(hierTOM, main="Cluster Dendrogram", labels=F, xlab="", sub="")
plotColorUnderTree(hierTOM,colors=data.frame(module=colorh1))
title("Module (branch) color")
dev.off()


pdf("TOM.pdf")
TOMplot(dissTOM , hierTOM, colorh1, terrainColors=TRUE) 
dev.off()


pdf("cmd.pdf")
cmd1=cmdscale(as.dist(dissTOM),3)
pairs(cmd1, col=as.character(colorh1),  main="MDS plot")
dev.off()


library(scatterplot3d)
pdf("3d.pdf")
par(mfrow=c(1,1), mar=c(4,3,2,3)+0.1)
scatterplot3d(cmd1,color=colorh1,angle=250,
xlab="Scaling Axis 1", ylab="Scaling Axis 2", zlab="Scaling Axis 3")
dev.off()


datME=moduleEigengenes(datExpr[,restConnectivity],colorh1)[[1]]
dissimME=1-(t(cor(datME, method="p")))/2
hclustdatME=hclust(dist(dissimME), method="average" )
pdf("modul_cluster.pdf")
par(mfrow=c(1,1))
plot(hclustdatME, main="Clustering tree based on the module eigengenes of modules")
dev.off()


pdf("modul_cor.pdf")
pairs(datME)
dev.off()


modul<-signif(cor(datME, use="p"), 2)
write.table(modul,"modul_cor.txt",sep="\t",quote=F)


datME=moduleEigengenes(datExpr,colorh1)[[1]]
color1=rep("grey",dim(datExpr)[[2]])
color1=as.character(colorh1)
datKME=signedKME(datExpr, datME)
datout=data.frame(datSummary, colorNEW=color1,datKME )
write.table(datout, "gene_module.xls", sep="\t", row.names=F,quote=F)


exportNetworkToCytoscape(ADJrest,edgeFile="edge.txt",nodeFile="node.txt",threshold = 0.5) 



inputclinial="clinical_data.txt" 
dataclinial = read.table(inputclinial,sep="\t",row.names=1,header=T,check.names=F,quote="!")
nGenes = ncol(datExpr)
nSamples = nrow(datExpr)
MEs0 = moduleEigengenes(datExpr,colorh1)$eigengenes
MEsFemale = orderMEs(MEs0)
modul_clinical_cor = cor(MEsFemale, dataclinial, use = "p")
write.table(modul_clinical_cor,"module-clinial-cor.xls",sep="\t",quote=F)
modul_clinical_p = corPvalueStudent(modul_clinical_cor, nSamples)
write.table(modul_clinical_p,"modul-clinical-p.xls",sep="\t",quote=F)
textMatrix = paste(signif(modul_clinical_cor, 2), " (", signif(modul_clinical_p, 1), ")",sep = "")
dim(textMatrix) = dim(modul_clinical_cor)
pdf("modul-clinical.pdf")
par(mar = c(6, 8.5, 3, 3))
labeledHeatmap(Matrix = modul_clinical_cor, xLabels = names(dataclinial), 
  yLabels = names(MEsFemale),ySymbols = names(MEsFemale), colorLabels = FALSE, 
  colors = greenWhiteRed(50), textMatrix = textMatrix, setStdMargins = FALSE, 
  cex.text = 1, zlim = c(-1,1), main = paste("Module-trait relationships"))
dev.off()


geneExp=datExpr
gene_clinial_cor = cor(geneExp, dataclinial, use = "p")
write.table(gene_clinial_cor,"gene_clinial_cor.xls",sep="\t",quote=F)
gene_clinical_p = corPvalueStudent(gene_clinial_cor, nSamples)
write.table(gene_clinical_p,"gene_clinical_p.xls",sep="\t",quote=F)