I am trying to identify clinically relevant hub genes using WGCNA. To do so, I have followed WGCNA tutorial, 'Network analysis of liver expression data from female mice'. I have used the step-by-step method and signed network to generate modules. However, I am confused about the way modules cluster together in the eigengene dendrogram. For example, the module lightgreen is highly correlated with the trait (image 1) but does not cluster together with that of the clinical trait in the eigengene dendrogram (image 2). As far as I understand, the module-trait relationship and eigengene dendrogram use eigengene values to establish the correlation with any given trait. If that's the case, I would expect lightgreen module clustered with that of the clinical trait in the dendrogram as well.
Similarly, the modules greenyellow and salmon which are clustered together with the trait, are not strongly correlated with the trait as seen in the module-trait relationships heatmap.
Below are the links for the images.
1. Image for module-trait relationship:
2. Image link for Eigengene dendrogram
3. Image link for Eigengene adjacency heatmap
4. Scale independence and mean connectivity
5. Number of genes assigned to the modules
I would really appreciate any explanation for this. I am confused and I am really not sure how do I choose the modules?
Thanks in advance