Hi
I am running WGCNA for module detection in some RNA-Seq data and I am having trouble picking a power for my data.
I have 24 samples and I am using a signed network. When I run the pickSoftThreshold function, I see that I would need a power of 30 or 32 for my data (which are the values that reach a scale free topology of 0.9). Is this power too high? Should I use a lower power for module detection?
In the WGCNA FAQ page, I saw that the authors recommend using a power of 18 for signed networks for a sample size between 20 and 30 in case the scale free topology fit index fails to reach values above 0.9 for reasonable powers (less than 15 for unsigned or signed hybrid networks, and less than 30 for signed networks). Is this the case of my data or should I be fine using a power of 30 or 32?
I tried running it with a power of 18 and a power of 30 (using deepSplit = 2 and minModuleSize = 20) but in both cases I have over 4,000 genes in module 0. Is that normal or is that something wrong with my data?
Any help is appreciated. Thank you!
Below are my codes and output for power detection:
powers = c(c(1:10), seq(from = 12, to=40, by=2))
sft1 <- pickSoftThreshold(datExpr0.2FDR, powerVector = powers, networkType ="signed")
Power SFT.R.sq slope truncated.R.sq mean.k. median.k. max.k.
1 1 0.00107 2.32 0.914 4750.00 4750.000 4900.0
2 2 0.18700 -9.38 0.860 2590.00 2570.000 2960.0
3 3 0.41000 -6.90 0.882 1510.00 1470.000 2000.0
4 4 0.55600 -5.08 0.902 923.00 885.000 1430.0
5 5 0.64600 -4.09 0.911 592.00 553.000 1070.0
6 6 0.69300 -3.49 0.910 393.00 358.000 831.0
7 7 0.73200 -3.07 0.912 270.00 237.000 658.0
8 8 0.78600 -2.72 0.932 190.00 161.000 532.0
9 9 0.82200 -2.51 0.942 138.00 112.000 436.0
10 10 0.85500 -2.35 0.955 101.00 79.100 363.0
11 12 0.88700 -2.16 0.965 58.30 41.300 261.0
12 14 0.87900 -2.16 0.960 35.60 22.800 203.0
13 16 0.87700 -2.13 0.960 22.80 13.100 162.0
14 18 0.88500 -2.09 0.969 15.20 7.780 133.0
15 20 0.88600 -2.05 0.970 10.50 4.770 111.0
16 22 0.88700 -2.01 0.973 7.53 3.000 93.8
17 24 0.86900 -2.00 0.962 5.51 1.940 80.5
18 26 0.88000 -1.94 0.968 4.14 1.280 69.8
19 28 0.89000 -1.88 0.970 3.16 0.867 61.2
20 30 0.89700 -1.84 0.970 2.47 0.595 54.1
21 32 0.90500 -1.80 0.974 1.95 0.414 48.1
22 34 0.91100 -1.75 0.975 1.57 0.293 43.1
23 36 0.93100 -1.70 0.982 1.28 0.210 38.8
24 38 0.94000 -1.65 0.983 1.06 0.152 35.2
25 40 0.94900 -1.61 0.988 0.88 0.111 32.0
It's normal for WGCNA to assign thousands of genes to the 0 (grey) module. WGCNA is a pretty robust analysis tool. You are likely to get many of the same modules when you run it with different powers (plus or minus a few hundred genes). I would probably use the lower power, because (from personal experience) 30-32 seems pretty high.