Is there any extension available/planned to the DESeq2 pipeline that would allow the inclusion of random effects in the model formula?
The dataset that I am trying to analyse includes repeat samples (at different time points) from the same individuals. However there are a large number of individuals (~200) and only a few samples per individual (total number of data points ~450). Therefore, I don’t believe that it would be advisable to fit individual as a fixed effect as it would likely lead to major overfitting. A random effect would seem perfect here and I have used it successfully in similar contexts with other simpler response variables. However as far as I am aware the standard DESeq function does not allow the inclusion of random effects. Based on other packages that fit negative binomial models, it seems that methods do exist for fitting random effects, but I don't know how to translate these to the context of DESeq.
Any suggestions much appreciated.