I want to try blockwise modules for WGCNA but I think I'm missing some points in logic.
If you have a dataset too large to run WGCNA (e.g. 100k genes) and want to run blockwise modules, how do you know which soft threshold to use to get a good scalefree topology for each of the networks?
If your data split into 10 blocks each with 10000 genes (block_1 - block_10) and block_1 - block_5 required a soft threshold of 6 to get a scalefree topology of 0.88 and block_6 - block_10 required a soft threshold of 11 to get a scalefree topology of 0.93. Would these subnetworks be comparable even though they have different scalefree topologies?
What function could you use to determine a soft threshold for each of the blocks individually?