Hello Bioconductor community!

I'm new to using these packages in R.

I am not working with gene expression data, but count data of amino acid sequences. I believe these packages provide the correct abstractions and methods to work with.

I have a number of different grouped samples (columns) with the same underlying proteins. Positive and negative controls are common and spiked into each sample. I have the row names for those.

I have run through calculating the logFC between sample groups, but this isn't what I actually want.

What I want to do is use these packages to run statistics on the fold change of a protein (gene) against the shared positive or negative controls, rather than between sample groups.

I think what I want to do is use the sample groupings to predict the protein-level dispersion, and then somehow either introduce a ratio to control into the glmFit design matrix, or new columns for the controls as new groups specifically in the design matrix.

I'm wondering if this seems feasible?

Warm regards.