WGCNA analysis for Proteomics data

Question

[Sumit Paliwal]

Hi Biostars community,

I have proteomic data on 60 samples. I want to perform WGCNA analysis on this data. I have referred to several tutorials and references including https://www.sciencedirect.com/science/article/abs/pii/S0076687916302890. However, there are several steps where I am having doubts.

1. To start with, should I use Log2 transformed intensities?
2. I have filtered the data (<30% NAs for each protein). This leaves me with only 180 proteins.
3. Not sure if I need to perform imputation on this filtered data.
4. While choosing soft-threholding power (using log2 transformed, non-imputed data), I am getting the following, so unable to decide what power should I choose.

>

 powers <- c(c(1:10), seq(from=12, to=30, by=2))

 SCA_sft <- pickSoftThreshold(SCA_proteome_60NGP_raw_filtered_t_log2, powerVector= powers, verbose = 5, networkType = "signed")

pickSoftThreshold: will use block size 180. pickSoftThreshold: calculating connectivity for given powers... ..working on genes 1 through 180 of 180

   Power SFT.R.sq   slope truncated.R.sq mean.k. median.k. max.k.
1      1   0.4000 34.7000          0.258   158.0     161.0  167.0
2      2   0.3680 12.9000          0.197   141.0     146.0  157.0
3      3   0.3340  8.1800          0.163   126.0     132.0  148.0
4      4   0.8440  2.6300          0.888   113.0     121.0  140.0
5      5   0.9080  1.9600          0.939   103.0     110.0  132.0
6      6   0.9600  1.5300          0.966    93.2     101.0  125.0
7      7   0.9430  1.2400          0.937    85.1      92.3  119.0
8      8   0.9330  1.0900          0.929    77.9      84.7  114.0
9      9   0.9330  0.8690          0.916    71.6      78.0  109.0
10    10   0.9090  0.7260          0.886    66.0      71.9  104.0
11    12   0.8080  0.4490          0.756    56.6      61.4   95.5
12    14   0.4450  0.2090          0.357    49.0      52.7   88.2
13    16   0.0741  0.0583         -0.101    42.8      45.4   81.9
14    18   0.0285 -0.0274         -0.212    37.7      39.3   76.3
15    20   0.3190 -0.1450          0.185    33.3      34.1   71.4
16    22   0.4860 -0.2250          0.340    29.7      29.7   66.9
17    24   0.5120 -0.3000          0.380    26.6      25.9   62.9
18    26   0.5790 -0.3570          0.473    23.9      22.7   59.3
19    28   0.6920 -0.4210          0.608    21.6      19.9   56.1
20    30   0.6820 -0.4690          0.602    19.6      17.5   53.1

1. Lastly, as there are only 180 proteins, can I keep the Module size to be 10 or 15?

Thanks in advance for your support