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?