What is the recommended method for specifying enriched or depleted features?

I'm following the tutorial here:
https://birdman.readthedocs.io/en/stable/default_model_example.html

With other software like ALDEx2 and edgeR you can specify a cutoff using FDR. I understand that working in a bayesian framework is different so I have a few questions:

* What is the recommended way for automating the feature sets using differentials?
* Is there an analog to an FDR cutoff that could be used that is generalizable to every run?
* Does this require manual curation on a case-by-case basis to determine which differentials are statistically enriched or depleted?

Start with the linked default model tutorial and review how it currently describes identifying enriched or depleted features. Check the project's existing documentation for Bayesian differential thresholds or feature-selection guidance. Done means the documentation answers the three questions with a reproducible recommendation, or clearly states when manual curation is required.

Written by the indexing model from the issue text.