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What data should I use to generate a gene coexpression network?

I have a microarray dataset that contains expression data of 30 samples of individuals with a certain disease and 30 samples of healthy individuals. After restricting this data to the most significantly differentially expressed genes, I want to use the WCGNA R package to perform a gene coexpression network analysis. Ideally, I want to look at the coexpression network of the disease data and the coexpression network of the healthy data separately, so I was thinking about separating the data and running WGCNA separately on both types of samples. Would this give me bad results? Do I need multiple types of samples to construct a correlation network?

Essentially, my question is: do I need different classes of samples to perform one coexpression network analysis, or could I construct a network for each class separately?

microarray co-expression r
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updated by Ram Ram • written by tahermun • tahermun

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I was thinking about separating the data and running WGCNA separately on both types of samples. Would this give me bad results?

I don't think so.

question is: do I need different classes of samples to perform one coexpression network analysis, or could I construct a network for each class separately?

In principle, you should construct the coexpression networks separately. Then only you would be able to compare the gene networks.

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updated by Ram Ram • written by Deepak Tanwar Deepak Tanwar

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