I have a microarray dataset which I was hoping to be able to process using a gene network construction algorithm, most notably WGCNA. I am having trouble determining if my current dataset is appropriate for network construction.
I have tried a number of different subsets of probes, samples, and also tried to use collapseRows, but I'm finding that the powers I would need to select in order to achieve a Scale Free Topology Model Fit Index of near 0.9 are extremely high- usually a soft-thresholding power of 25 or greater. Comparatively, in the WGCNA tutorials and other material I've seen, common powers are between 6 and 10.
I know that if the Model fit index isn't high, the network won't approximate a scale-free topology and the connectivity will be too high to be useful. However, I haven't figured out what factors in the dataset would be contributing to this. Admittedly, my sample size is small- only 11 samples. However, I didn't see any recommendations for determining minimum sample size, nor any way to calculate that. Does anyone have any sorts of 'best practices' regarding this for WGCNA? Should I go ahead and run through the rest of the WGCNA workflow even if I need to select a power of 30 or so to get a Topology Model Fit Index near the suggested 0.9?
We have a number of datasets we'd like to apply this to, but I'm getting concerned now because we usually only have three biological replicates, and typically only a few conditions to test. If this isn't going to work, I'll need to find a similar method that is more robust to smaller sample sizes, even if it's less effective overall compared to WGCNA.