Hi, first of all, thank you for this great pipeline. In our centre we are usually using DIA-NN (or specifically bigBio:quantmsdiann) output for proteomics data, which requires some normalisation. I previously implemented a new 'study_type' for general 'mass_spec' with a simple count matrix in a fork and added different limma normalisation methods for it (including variance stabilised normalisation). This was version 1.5.0. I started porting this to the version 2, but I realised that you completely removed any non nf-core modules. Before I would get it ready for a PR, in case this is of general interest, would it be okay to add the limma normalisation processes simply in

modules/local

?

All this stuff is handled through the central nf-core subworkflow now: github.com/nf-core/differentialabundance/blob/…/main.nf Any changes you want to propose would need to be proposed against that central subworkflow and/ or its component modules first, definitely not local modules (we went to quite some effort to strip those, as you've seen). But I think you should be able to do most of what you need with just

--study_type generic_matrix --differential_method limma

? The limma module is already doing normalisation internally. If you want to propose tweaks on how that's done just make a PR to the module (in nf-core/modules first), and ping us here for review.

Okay, thank you. The implemented normalisation is not ideal for mass spec data. I think it would also be better to have a dedicated process just for normalisation to increase modularity. I will send a PR for the limma module and get back to you