Hi @NicolasB - although we did not include rarefaction in our own evaluation, a recent preprint concluded that MaAsLin 2 (particularly with rarefied data) could also be a reasonable choice for users looking for increased statistical power at the potential cost of more false positives.
Coming back to your question, you are right that rarefied data can be considered normalized data and likewise, you don’t need additional normalization before statistical modeling.
As for alternative models/transformations, we usually do not recommend a particular combination over another as the choice is usually problem- and data-specific. Apart from trying out various transformations with the LM models, you can also consider other non-LM models without normalization/transformation and see if that supports your hypothesis.
Check out the following discussions for some more insights:
Best,
Himel