WGCNA Setting Hard Threshold Error

Question

[Al Murphy]

I have what I imagine is a very simple error regarding the use of WGCNA in R. I am trying to create a weighted correlation network by setting a hard threshold using the function pickHardThreshold. However, when I pass in my expression set I get a strange error:

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  NA/NaN/Inf in 'x'

I imagine there is something wrong with my understanding of how to use the function but there seems to be no tutorials or examples where a hard threshold is set (there are many based on soft thresholds, such as here).

I am passing in an expression set matrix with the samples as the rows and the genes as the columns as described in the documentation for the function

expression data in a matrix or data frame. Rows correspond to samples and columns to genes.

Also, there are no NA's in my dataset which was my first check based on the error. Just to give a reproducible example of the error see below:

exp_matrix<-matrix(data=runif(100),ncol=10,
               dimnames=list(paste0("sample_",1:10),
                          paste0("gene_",1:10)))

pickHardThreshold(exp_matrix)

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : NA/NaN/Inf in 'x'

Any help would be greatly appreciated!

Thanks.

Answer 1

[andres.firrincieli]

How big is the original dataset? I am asking because by increasing the number of genes, the function works just fine

 > exp_matrix<-matrix(data=runif(10000),ncol=1000,
    +                    dimnames=list(paste0("sample_",1:10),
    +                                  paste0("gene_",1:1000)))
    > pickHardThreshold(exp_matrix)
        Cut p.value SFT.R.sq  slope. truncated.R.2 mean.k. median.k. max.k.
    1  0.10 0.77000 0.129000  18.000        0.9750 781.000     781.0    825
    2  0.15 0.66000 0.113000 -13.000        0.9570 676.000     676.0    736
    3  0.20 0.55500 0.160000 -11.600        0.9620 575.000     575.0    640
    4  0.25 0.45800 0.161000 -10.600        0.9820 482.000     482.0    543
    5  0.30 0.37000 0.033700   4.040        0.9830 396.000     395.0    450
    6  0.35 0.29100 0.000637  -0.451        0.9830 319.000     319.0    367
    7  0.40 0.22300 0.130000   4.460        0.9660 250.000     251.0    286
    8  0.45 0.16500 0.150000   4.620        0.9090 191.000     191.0    227
    9  0.50 0.11700 0.129000  -3.820        0.9720 141.000     141.0    178
    10 0.55 0.07960 0.014000  -0.927        0.9780 100.000     101.0    129
    11 0.60 0.05100 0.009720  -0.618        0.9860  68.300      68.0     94
    12 0.65 0.03040 0.090100  -1.730        0.9930  43.400      43.0     70
    13 0.70 0.01650 0.166000  -1.540        0.9710  25.800      25.5     46
    14 0.75 0.00785 0.251000  -1.540        0.9820  13.600      13.0     29
    15 0.80 0.00311 0.221000  -3.510        0.2660   6.310       6.0     20
    16 0.85 0.00092 0.741000  -2.380        0.9680   2.240       2.0     10
    17 0.90 0.00016 0.066100  -2.640       -0.0933   0.478       0.0      4
    $cutEstimate
    [1] NA

    $fitIndices
        Cut      p.value     SFT.R.sq      slope. truncated.R.2 mean.k. median.k. max.k.
    1  0.10 0.7698749999 0.1290790714  17.9939990    0.97500919 781.016     781.0    825
    2  0.15 0.6597824485 0.1133110002 -13.0200677    0.95706793 675.634     676.0    736
    3  0.20 0.5554454421 0.1596124481 -11.6012789    0.96210336 575.292     575.0    640
    4  0.25 0.4584436539 0.1614028198 -10.5888632    0.98160376 481.764     482.0    543
    5  0.30 0.3700831223 0.0337023377   4.0407518    0.98286505 395.628     395.0    450
    6  0.35 0.2913484779 0.0006370488  -0.4512638    0.98253470 318.634     319.0    367
    7  0.40 0.2228683501 0.1297646029   4.4609108    0.96566340 250.474     251.0    286
    8  0.45 0.1648959909 0.1497857583   4.6248899    0.90927965 190.910     191.0    227
    9  0.50 0.1173068030 0.1289897777  -3.8171609    0.97150651 140.946     141.0    178
    10 0.55 0.0796140219 0.0139719875  -0.9272260    0.97817099 100.418     101.0    129
    11 0.60 0.0510032607 0.0097188946  -0.6175709    0.98593371  68.276      68.0     94
    12 0.65 0.0303859638 0.0900913765  -1.7328536    0.99324252  43.444      43.0     70
    13 0.70 0.0164709788 0.1658053604  -1.5374235    0.97092572  25.808      25.5     46
    14 0.75 0.0078523983 0.2505013985  -1.5377157    0.98227575  13.592      13.0     29
    15 0.80 0.0031104283 0.2207179089  -3.5116139    0.26596937   6.306       6.0     20
    16 0.85 0.0009201220 0.7413987164  -2.3807465    0.96838864   2.242       2.0     10
    17 0.90 0.0001599714 0.0660983194  -2.6389376   -0.09327525   0.478       0.0      4

[Al Murphy]

How odd, I have 500 genes and 100 samples so it would be a 100x500 matrix. I've checked on that size and I hit the same issue

[Al Murphy]

Sorry just to add to this, when you increase your example to 100 samples rather than 10 it gives the same error. So it seems its not just down to the number for genes.

[andres.firrincieli]

> pickHardThreshold(exp_matrix, cutVector = seq(0.1, 0.6, by = 0.1))
  Cut p.value SFT.R.sq slope. truncated.R.2 mean.k. median.k. max.k.
1 0.1   0.770 0.032300   5.66        0.1860     6.6       7.0      8
2 0.2   0.555 0.001530  -0.64        0.0390     4.6       5.0      7
3 0.3   0.370 0.000171  -0.14        0.1310     3.0       3.5      4
4 0.4   0.223 0.014800  -1.32       -0.1340     2.2       2.0      4
5 0.5   0.117 0.030900  -1.89       -0.0361     1.0       1.0      3
6 0.6   0.051 0.113000  -3.05        0.7060     0.2       0.0      1
$cutEstimate
[1] NA

$fitIndices
  Cut    p.value     SFT.R.sq     slope. truncated.R.2 mean.k. median.k. max.k.
1 0.1 0.76987500 0.0323011476  5.6557635    0.18627071     6.6       7.0      8
2 0.2 0.55544544 0.0015318734 -0.6395621    0.03898946     4.6       5.0      7
3 0.3 0.37008312 0.0001711509 -0.1400856    0.13106798     3.0       3.5      4
4 0.4 0.22286835 0.0148168861 -1.3219051   -0.13430132     2.2       2.0      4
5 0.5 0.11730680 0.0308509930 -1.8857022   -0.03613967     1.0       1.0      3
6 0.6 0.05100326 0.1133166019 -3.0497797    0.70582124     0.2       0.0      1

> pickHardThreshold(exp_matrix,  = seq(0.1, 0.7, by = 0.1))
  Cut p.value SFT.R.sq slope. truncated.R.2 mean.k. median.k. max.k.
1 0.1  0.7700 0.032300   5.66        0.1860     6.6       7.0      8
2 0.2  0.5550 0.001530  -0.64        0.0390     4.6       5.0      7
3 0.3  0.3700 0.000171  -0.14        0.1310     3.0       3.5      4
4 0.4  0.2230 0.014800  -1.32       -0.1340     2.2       2.0      4
5 0.5  0.1170 0.030900  -1.89       -0.0361     1.0       1.0      3
6 0.6  0.0510 0.113000  -3.05        0.7060     0.2       0.0      1
7 0.7  0.0165 0.113000  -3.05        0.7060     0.2       0.0      1
$cutEstimate
[1] NA

$fitIndices
  Cut    p.value     SFT.R.sq     slope. truncated.R.2 mean.k. median.k. max.k.
1 0.1 0.76987500 0.0323011476  5.6557635    0.18627071     6.6       7.0      8
2 0.2 0.55544544 0.0015318734 -0.6395621    0.03898946     4.6       5.0      7
3 0.3 0.37008312 0.0001711509 -0.1400856    0.13106798     3.0       3.5      4
4 0.4 0.22286835 0.0148168861 -1.3219051   -0.13430132     2.2       2.0      4
5 0.5 0.11730680 0.0308509930 -1.8857022   -0.03613967     1.0       1.0      3
6 0.6 0.05100326 0.1133166019 -3.0497797    0.70582124     0.2       0.0      1
7 0.7 0.01647098 0.1133166019 -3.0497797    0.70582124     0.2       0.0      1

If I modify cutVectorI am able to run the function without errors. So i guess the error happen when pickHardThreshold attempt to calculate the scale free topology fit from scaleFreeFitIndex(k = khelp, nBreaks = nBreaks, removeFirst = removeFirst) using a vector k of connectivities which, in your case, seems to be Zero. Funny enough, this does not happen if you use the function pickSoftThreshold.

I would post the same question on bioconductor. WGCNA devs are more active there

[Al Murphy]

Perfect, I will do. Thank you very much for all the help!