Hi everyone,
I am performing WGCNA analysis to construct a gene network. My data is in the correct format, and I have already performed quality control to retain good genes and samples. Here is a small sample of my dataset:
DPM1 SCYL3 C1orf112 FGR CFH
0GbeZp_h 0.2064612 0.4053029 -0.8209627 -1.4753389 -1.6783116
0JYiZn~F -1.4407546 -0.8980612 0.3089006 -0.6724426 1.6265530
0OhjHlow 0.2277668 -0.3659655 1.6719226 0.7007402 -1.1298345
0S821Bnt -0.6518080 -1.2202983 0.6172766 0.1892329 0.7054255
0T7K5SX1 1.8057810 -1.5028378 -0.4203526 -0.1229333 2.8449602
Next, I attempted to determine the soft-thresholding power using the following R code:
powers <- c(1:15) # Testing a range of powers
sft <- pickSoftThreshold(data_t, powerVector = powers, verbose = 5)
The function runs but does not return a valid power estimate. Here is the output:
pickSoftThreshold: using block size 677.
pickSoftThreshold: calculating connectivity for given powers...
..working on genes 1 through 677 of 677
Power SFT.R.sq Slope Truncated.R.sq Mean.k Median.k Max.k
1 1 0.0310 5.02 0.987 1.53e+01 1.53e+01 1.68e+01
2 2 0.0235 -2.36 0.985 5.42e-01 5.41e-01 6.57e-01
3 3 0.0372 -1.96 0.980 2.45e-02 2.43e-02 3.28e-02
4 4 0.0621 -1.66 0.964 1.30e-03 1.29e-03 1.92e-03
5 5 0.2350 -2.47 0.933 7.80e-05 7.70e-05 1.36e-04
6 6 0.4920 -3.10 0.823 5.15e-06 5.02e-06 1.13e-05
7 7 0.3530 -6.43 0.277 3.69e-07 3.54e-07 1.10e-06
8 8 0.3770 -7.32 0.219 2.82e-08 2.59e-08 1.17e-07
9 9 0.4460 -6.88 0.344 2.28e-09 1.99e-09 1.34e-08
10 10 0.4760 -7.20 0.347 1.93e-10 1.55e-10 1.60e-09
11 11 0.4710 -6.39 0.345 1.71e-11 1.30e-11 1.96e-10
12 12 0.4880 -6.42 0.343 1.57e-12 1.07e-12 2.46e-11
13 13 0.4720 -5.78 0.327 1.49e-13 9.04e-14 3.12e-12
14 14 0.4270 -5.30 0.272 1.46e-14 7.77e-15 3.99e-13
15 15 0.4620 -5.07 0.309 1.46e-15 6.66e-16 5.13e-14
At the end of the output, I receive:
$powerEstimate
[1] NA
Since no valid power estimate is found, I am unable to proceed with WGCNA, as no modules can be detected.
Why might the power estimate be NA, and how can I fix it?
Are there any preprocessing steps I should double-check?
Any insights or suggestions would be greatly appreciated! Thank you in advance.
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Interesting data! Looking at these genes, it's like navigating a complex maze. The fluctuating values remind me of chasing high scores in pacman 30th anniversary . Each gene's power and slope seem to impact the truncated R-squared value significantly. It'd be great to see this visualized to better understand the relationships. Are there any particular genes you're focusing on?
Hi Maria, your dataset looks clean! It's great you've already done the quality control steps. I've had similar issues with pickSoftThreshold sometimes. It might be worth trying a wider range of powers, perhaps extending it beyond 15, just to see if you catch anything. Also, double-check that data_t is indeed your transposed data matrix, and doesn't contain any unusual values (NaNs or infinities). I know it sounds silly, but sometimes a quick sanity check helps. Think of it like making sure your Eggy Car is fueled up before a long drive - small details can make a big difference! Good luck!