Hey, I am currently working on a project analyzing differential translation efficiency (TE) between wild-type and treatment conditions in a specific cell type.

To calculate TE, I have generated gene-level counts using STAR and HTSeq from both ribosome profiling (Ribo-seq) and RNA sequencing (RNA-seq) data. I'm considering using DESeq2 to analyze differential expression of these TE values between the conditions to identify genes with significant changes in TE.

Given that TE is represented as the ratio of Ribo-seq counts to RNA-seq counts for each gene, I would appreciate your advice on the best approach for this analysis using DESeq2. Specifically, I would like to understand:

1. How should I structure the input data for DESeq2 when using TE as the response variable?
2. Is DESeq2 appropriate for this type of analysis, or would you recommend alternative methods or considerations?
3. Can DESeq2 handle input where TE = (Ribo-seq counts / RNA-seq counts) for each gene in the wild-type and treatment conditions, even though this isn't traditional RNA-seq count data?
4. If DESeq2 isn't suitable, how might I identify genes with significant changes in TE, given that a t-test hasn't yielded significant results?

Thanks in advance, Noi.

There are a number of similar posts on the support site, could you check those out first? E.g. I see them on the right hand side of the screen when looking at this post.

Also:

DESeq2 testing ratio of ratios (RIP-Seq, CLIP-Seq, ribosomal profiling)