This is my first time working on using WGCNA on a microarray dataset. One of the major problems I am facing is merging close modules which is not really working well. I have quantile normalised the data before working on it. I have put my code below and link to some of the plots generated during this process. I was wondering can somebody advice on what could be improved upon in this analysis? Link to plots: ![plots] https://i.sstatic.net/dF6NA.jpg I have a total of 767 samples and 13466 probes.
powers = c(c(1:10))
## Scale free topology since it can find hubs
sft = pickSoftThreshold(bryois_norm_keep_use, powerVector = powers, verbose = 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")
# 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")
# Turn data expression into topological overlap matrix
power=sft$powerEstimate
# Even though estimate suggests 3, trying ahigh number
# Turn adjacency into topological overlap matrix (TOM)
adjacency <- adjacency(bryois_norm_keep_use, power = 6)
TOMadj <- TOMsimilarity(adjacency,verbose = 5)
dissTOMadj <- 1- TOMadj
# Clustering using TOM
# Call the hierarchical clustering function
hclustGeneTree <- hclust(as.dist(dissTOMadj), method = "average")
# Plot the resulting clustering tree (dendogram)
sizeGrWindow(12, 9)
plot(hclustGeneTree, xlab = "", sub = "",
main = "Gene Clustering on TOM-based disssimilarity",
labels = FALSE, hang = 0.04)
# Make the modules larger, so set the minimum higher
minModuleSize <- 30
# Module ID using dynamic tree cut
dynamicMods <- cutreeDynamic(dendro = hclustGeneTree,
distM = dissTOMadj,
deepSplit = 2, pamRespectsDendro = FALSE,
minClusterSize = minModuleSize,verbose = 5)
table(dynamicMods)
# Convert numeric lables into colors
dynamicColors <- labels2colors(dynamicMods)
table(dynamicColors)
# Plot the dendrogram and colors underneath
sizeGrWindow(8,6)
plotDendroAndColors(hclustGeneTree, dynamicColors, "Dynamic Tree Cut",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05,
main = "Gene dendrogram and module colors")
# Calculate eigengenes
dynamic_MEList <- moduleEigengenes(bryois_norm_keep_use, colors = dynamicColors)
dynamic_MEs <- dynamic_MEList$eigengenes
library(dynamicTreeCut)
# Calculate dissimilarity of module eigengenes
dynamic_MEDiss <- 1-cor(dynamic_MEs)
dynamic_METree <- hclust(as.dist(dynamic_MEDiss))
# Plot the hclust
sizeGrWindow(7,6)
plot(dynamic_METree, main = "Dynamic Clustering of module eigengenes",
xlab = "", sub = "")
######################## MERGE SIMILAR MODULES
dynamic_MEDissThres <- 0.95
# Plot the cut line
#abline(h = dynamic_MEDissThres, col = "red")
# Call an automatic merging function
merge_dynamic_MEDs <- mergeCloseModules(bryois_norm_keep_use, dynamicColors, cutHeight = dynamic_MEDissThres, verbose = 5)
# The Merged Colors
dynamic_mergedColors <- merge_dynamic_MEDs$colors
# Eigen genes of the new merged modules
mergedMEs <- merge_dynamic_MEDs$newMEs
mergedMEs
table(dynamic_mergedColors)
sizeGrWindow(12,9)
plotDendroAndColors(hclustGeneTree, cbind(dynamicColors, dynamic_mergedColors),
c("Dynamic Tree Cut", "Merged dynamic"),
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
