WGCNA blockwiseModules function for small datasets

Question

[ATS]

Hi,

I'm doing WGCNA on a relatively small dataset (~1500 genes and 50 samples). All of the tutorials that I can find are using the blockwiseModules function in the WGCNA package for R, which seems great for large datasets. Since I'm working with a smaller dataset, though, I'm not super concerned about RAM.

Is there anything lost by using the blockwiseModules function on a small dataset? Would the alternative be to use several separate functions instead (to calculate adjacency, TOM, merge, etc), and if so is there a benefit to doing the analysis that way?

Thanks for your help! This community has been a lifesaver!

Answer 1

[LChart]

You lose some granular control over parameters. If you set the block to be larger than the number of genes, you just get a "default" run of WGCNA, modified by the parameters you put as input. Also the saved TOM is nested in a list, so it can be somewhat unwieldy at first. But it's a great "fast path" to a WGCNA network if you set the block size to inf.

[ATS]

That's great to know. Thanks for your input!