I am performing a network analysis on WGCNA. I had to separate the samples into 2 networks (depending on the tissue) due to the high variation between tissues. Each network is made by 15 samples (which is supposed to be the minimun acceptable).
In FAQ of WGCNA it is recommended to use bicor correlation rather than pearson correlation. However my data from the ovaries seems to perform better with pearson correlation. (There isnt much difference on the samples from the body).
I send attached the graphs of scale independence of the ovary network when using pearson and bicor.
In this case, should I use pearson? or use bicor even if the adjustment to the scale independence seems to be worse?
Also, from the module-trait graph I obtain certain Pvalues associated to each module and some of them are lower than 0.05. However if I adjust the Pvalue by bonferroni (or any other method) and the total number of modules, none of them becomes significant. Does it mean my data is useless? Can I use in further analysis the modules that are significantly associated to any of the traits with uncorrected Pvalues? I want to check for TF associated to the genes in each module to look for regulators of the associated traits (fecundity and lifespan). Should I modify any parameter to make them most significant? By now I have normalized the data (RNAseq data) according to what is said on FAQ on WGCNA page, I choose the soft threshold highlighted by the function picksoftthreshold and I am using signed hybrid networks. All the other parameters are the same than the default ones choosen on the tutorials.
Thank you very much!