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2 hours ago by

Hi everyone. I'm running WGCNA on multiple expression datasets representing 3 different brain regions with roughly 40 samples per set and I'm having difficulty understanding the parameter selection prior to network construction, specifically when choosing the power. If I'm interpreting the Scale Free Analysis plots correctly, it seems I should choose a power of 3 since that is the first value to cross 0.9 for the model fit index and is the rough inflection point on the mean connectivity plot.


After running the analysis using 3 for the power parameter, I get around 20 modules with the 1st (turquoise) module containing roughly half the genes in my dataset. Maybe this is okay, but my intuition is telling me maybe choosing a power so low is leading to overclustering leading to some of the larger clusters potentially being biologically meaningless. I'm hesitant to change it, however, given the results of the plots above.

I've read the tutorials and a ton of help threads online, but I haven't seen any example plots or help suggestions that recommend a power as low as 3. Is there a minimum power that I shouldn't go below or should I just trust the QC plots and go with 3?


modified 2 hours ago

2 hours ago


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