3
$\begingroup$

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.stack.imgur.com/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)
$\endgroup$

1 Answer 1

3
$\begingroup$

I took a quick look and don't see anything wrong with the module merging. You could lower the merging threshold a bit - 0.95 is very high, I would use 0.9 or 0.85. Other than that though, really nothing seems out of the ordinary.

$\endgroup$
1
  • $\begingroup$ Hi Peter, Sorry for the late reply but thank you again for your feedback. :) $\endgroup$ Sep 30, 2022 at 8:16

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.