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While choosing the soft-threshold, I observe that for my data (45 samples, case+control), the soft threshold does not reach R^2 ~ 0.8 for unsigned network + bicor but flattens around 0.7. Though a fit of 0.8 would have been definitely better, is it advisable to go with power at 0.7?
I did came across that but still have some confusion.
Genes were only filtered to remove low counts.
The experimental design had batch effect which was accounted for. Hierarchical clustering after batch correction showed the sample grouping as desired based on the actual covariate.
This might be good when we at all do not reach any suitable power and for exploratory analysis. In my case, starting power = 14 the fit saturates around 0.7. Now, based on a sample size, if I choose power = 6, that means a much lower fit to scale-free topology. Which would be better in this case?