Hello, I've reviewed A: WGCNA Hub Gene Selection Method. I'm calculating module membership (MM) using geneModuleMembership = as.data.frame(cor(datExpr, MEs, use = "p"))

Because kME is a more sophisticated approach as it includes error checking and other features, I also used signedKME( datExpr, MEs, outputColumnName="KME", corFnc="bicor")

For this I'm using reads from the affy hgu133plus2 platform. As a test, I examined the three probes that target a certain gene, and found that the most significant MM correlation was to the tan module in three cases , whereas the kME significance was tan, tan, and dark orange.

However, the dendrogram tree cutting places these three probes in green, gray, and tan. Which of these calculations should I use as the final call for which module a probe most significantly resides within?

Also, kIM only calculates the connectivity of a probe within its home "dendrogram" module, so I'm not easily able to compare kIM values within all the modules? Dr. Langfelder writes kME and kIM are usually very similar, but I have no way to establish this?

Thanks much,

Robert Robl

I am in a similar point now, so I will post what I understood.

The dendogram's modules are built with the TOM values, while the kIM and kME, are built with the adjacency. So here come the main differences in which module are probes.

The kIM value asses how well connected it is within a module, so there is no point to compare with all the modules. While kME, asses how similar it is to other eigengenes, all of them the one in which it is assigned or others. To compare them use the kME for the module where the probes are against the kIM. In the tutorial of Simulated data 07, there is a section comparing them, you can use the code from there. However, I must admit that I found strange that when comparing kME to kIM, kME is to the 6th power, and I couldn't find a reason. I found it is the same power used to build the network, but I don't know the rationale behind this.