Soft-threshold in WGCNA
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ag1805x ▴ 50
@ag1805x-15215
Last seen 7 days ago
University of Allahabad

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?

wgcna network bicor threshold • 273 views
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@peter-langfelder-4469
Last seen 12 months ago
United States

Please see WGCNA FAQ, point 6.

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I did came across that but still have some confusion.

the user should ensure that variables (probesets, genes etc.) have not been filtered by differential expression

Genes were only filtered to remove low counts.

If the scale-free topology fit index fails to reach values above 0.8 for reasonable powers ... chances are that the data exhibit a strong driver that makes a subset of the samples globally different from the rest.

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.

the appropriate soft-thresholding power can be chosen based on the number of samples

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?

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