I have a dataset of 50 breast cancer samples. These samples are classified into four subtypes Lum A, Lum B, Her2 and Basal. I have been working with lncRNAs and protein-coding genes. To identify the functions of lncRNAs, I have used WGCNA through which I get the modules with protein-coding genes and lncRNAs. The expression data is stored in a matrix
I have the trait information in
datTraits like below:
I totally got 20 modules named with different colors stored in
dynamicColors. Like below I first calculated eigenvalues and then used it and trait information to check the association between Modules and trait data.
MEs0 = moduleEigengenes(datExpr, dynamicColors)$eigengenes MEs = orderMEs(MEs0) moduleTraitCor = cor(MEs, datTraits, use = "p"); moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nSamples);
What is the optimal method to associate modules and trait using binary data?
Is Pearson correlation a right way to check the association?
Is there any other way to check the association between modules and trait (which represented like above in binary)? Details of methology would be welcome.
Patients are not genetically related and I have no information on environmental risk factors. I was thinking about logistic regression, but before doing that, I would like to take suggestions.