Wanted to raise a discussion about pros and cons for different methods for testing a feature association with metadata column (e.g. change of any ASV's in my samples with change in weight during a study).
The way I see it, there are 3 main options:
- Using linear mixed model, for example from QIIME longitudinal plugin. From my understanding there are 2 major limitations: associations between microbial features and metadata columns would probably not be normally distributed; and I don't if there is an option to "run" over each feature in a feature table to check the model for each feature separately (and correct for multiple comparisons obviously).
- Using a popular regression package for microbiome data, for example Maaslin2. Major downside is that it is not in QIIME
- Using one of the machine learning prediction models QIIME offers, which is a bit different ballgame and doesn't provide P (or q) values.
Would love to hear people opinions and experiences of why choosing one method over the other, some other pros and cons, and other methods for testing feature-outcome associations.