*This is a copy of this post https://github.com/drisso/zinbwave/issues/58#issue-656953981 but I thought it might reach out to more people by posting it here too.*

Hi everyone,

I am measuring differential expression between two conditions in 10X data, using zinbwave and DESeq2. I am using K=2 in zinbwave as there are some latent factors (e.g. library size) that I would like to infer from the data. Now, would it make sense to include W1 (as calculated with zinbwave) in the DESeq2 design model (something like `design <- model.matrix(~condition + reducedDim(exp, "zinbwave")[,1])`

? Or is W somehow already reflected in the observational weights, in which case including it in the model would be redundant?

Similarly, as I use the LRT test in DESeq2 should I include W in the reduced formula?

Many thanks for your input! Vincent

Mike is right. The observation weights only account for the zero inflation, so if you want to account for the latent factor in the DE model you have to include W in both the full and reduced model.

That makes sense. Thank you both!