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I have been using DESeq2 and limma to identify differentially expressed genes in a dataset. I would like to do something similar for binary variables, such as mutations, using a logistic regression model.

In DESeq2 and limma, each gene is fit by a seperate model. They use empirical bayes shrinkage to moderate residual variances of the linear models, borrowing information between genes (models) and improving parameter estimates with small sample sizes.

Can this approach be applied to logistic regression?

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  • $\begingroup$ Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. $\endgroup$
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    Commented Aug 2 at 1:00

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