Combining different data types into a single matrix for WGCNA using DESeq2 normalization
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8 weeks ago
jshin • 0

Hi,

I have two different RNA-seq datasets for the sample set of samples, generated using mRNA-seq and smallRNA-seq. The goal here is to identify a set of coding-genes and smallRNAs (no known function/targets) that act together. One of the approaches I'm considering is applying WGCNA on each dataset and use correlation to associate gene modules found in each data type.

Another way I'm considering is applying WGCNA on a concatenated data matrix, where DESeq2 normalization is applied to each data type (mRNA and smallRNA) independently and then combined to create a single matrix, which can then be used as an input for WGCNA. What I wasn't sure was if combining counts that came from different sequencing methods and were normalized independently is reasonable. I'm having a hard time coming up with a logical explanation for why it's incorrect to do this (or why it's okay to do this) so if anyone can help out, would very much appreciate it!

WGCNA DESeq2 smRNA normalization • 142 views
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