library(WGCNA)
library(flashClust)
options(stringsAsFactors = FALSE);
countdata <- read.csv('module500.csv', header=TRUE)
dim(countdata)
names(countdata)
femData <- countdata
names(femData)
datExpr = as.data.frame(t(femData[, -c(1)]))
View(datExpr)
dim(datExpr)
names(datExpr) = femData$Symbol
View(datExpr)
rownames(datExpr) = names(femData)[-c(1)]
dim(datExpr)
powers = c(c(1:10), seq(from = 12, to=20, by=2))
sft=pickSoftThreshold(datExpr,dataIsExpr = TRUE,powerVector = powers,corFnc = cor,corOptions = list(use = 'p'),networkType = "unsigned")
sizeGrWindow(9, 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")

# Red line corresponds to using an R^2 cut-off
abline(h=0.80,col="red")

# Mean connectivity as a function of the soft-thresholding power
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")

softPower = 7

adj= adjacency(datExpr,type = "unsigned", power = softPower)

#turn adjacency matrix into topological overlap to minimize the effects of noise and spurious associations
TOM=TOMsimilarityFromExpr(datExpr,networkType = "unsigned", TOMType = "unsigned", power = softPower)

colnames(TOM) =rownames(TOM) = datExpr
dissTOM=1-TOM

geneTree = flashClust(as.dist(dissTOM),method="average")
plot(geneTree, xlab="", sub="",cex=0.3)


minModuleSize = 20;

# Module identification using dynamic tree cut

dynamicMods = cutreeDynamic(dendro = geneTree,  method="tree", minClusterSize = minModuleSize);
#dynamicMods = cutreeDynamic(dendro = geneTree, distM = dissTOM, method="hybrid", deepSplit = 2, pamRespectsDendro = FALSE, minClusterSize = minModuleSize);

#the following command gives the module labels and the size of each module. Lable 0 is reserved for unassigned genes
table(dynamicMods)
dynamicColors = labels2colors(dynamicMods)
table(dynamicColors)

plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut", dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05, main = "Gene dendrogram and module colors")

restGenes= (dynamicColors != "grey")
diss1=1-TOMsimilarityFromExpr(datExpr[,restGenes], power = softPower)


colnames(diss1) =rownames(diss1) = datExpr[restGenes]
hier1=flashClust(as.dist(diss1), method="average" )
plotDendroAndColors(hier1, dynamicColors[restGenes], "Dynamic Tree Cut", dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05, main = "Gene dendrogram and module colors")
diag(diss1) = NA
sizeGrWindow(7,7)
TOMplot(diss1, hier1, as.character(dynamicColors[restGenes]))

module_colors= setdiff(unique(dynamicColors), "grey")
for (color in module_colors){
  module=datExpr[which(dynamicColors==color)]
  write.table(module, paste("module_",color, ".txt",sep=""), sep="\t", row.names=TRUE, col.names=TRUE,quote=FALSE)
}

module.order <- unlist(tapply(1:ncol(datExpr),as.factor(dynamicColors),I))
m<-t(t(datExpr[,module.order])/apply(datExpr[,module.order],2,max))
heatmap(t(m),zlim=c(0,1),col=gray.colors(100),Rowv=NA,Colv=NA,labRow=NA,scale="none",RowSideColors=dynamicColors[module.order])


MEList = moduleEigengenes(datExpr, colors = dynamicColors)
MEs = MEList$eigengenes
plotEigengeneNetworks(MEs, "", marDendro = c(0,4,1,2), marHeatmap = c(3,4,1,2))

So this is my code im using for WGCNA ,but at this point abline(h=0.80,col="red") i not getting an abline in SFT plot ,am i doing something wrong or what is the issue.

Any help or suggestion would be highly appreciated