Pairwise correlation among genes within a module using WGCNA

Question

[waterwasser10]

This is regarding the package WGCNA (Weighted gene coexpression network analysis) in R. I have been trying to use this package on my data.

I use the blockwiseModules() function to get my modules. WGCNA identified some modules. When I look into the membership values of the genes, they are high. On the other hand, when I do the pairwise correlation analysis of the genes within a particular module, they are extremely low. I get the modules using $colors so they are after merging as far as my understanding goes. I tried the same with the sample data provided with the package (female mice liver data) and I observe similar pattern, they are not as low as they are in my data but they are all not above 0.8 which is my cut off (merCutHeight=0.2). I am unable to understand this statistical point of the results. Has someone faced a similar situation when working with the package? Can someone throw some light on this.

Thanks in advance.

Answer 1

[Lluís R.]

In the latest version, it says about mergeCutHeight

In the last step, modules whose eigengenes are highly correlated are merged. This is achieved by clustering module eigengenes using the dissimilarity given by one minus their correlation, cutting the dendrogram at the height ‘mergeCutHeight’ and merging all modules on each branch. The process is iterated until no modules are merged. See ‘mergeCloseModules’ for more details on module merging.

So is not merging genes but merging similar modules.