Hypothesis
Statistical methods built specifically for sparse, compositional microbiome data would identify differentially abundant taxa better than methods borrowed from bulk and single-cell RNA-seq.
What failed
Benchmarked on 100 manually curated 16S and shotgun metagenome datasets for distributional fit, false discovery control, concordance, power and correct genus calls, the multivariate and compositional microbiome methods did not outperform univariate RNA-seq differential expression methods.
Broken assumption
Modelling compositionality and sparsity explicitly gives better differential abundance inference on real microbiome data.
Why
The abstract gives no mechanism; the authors recommend dataset-specific exploratory analysis before picking any inferential model.
From the published paper: Calgaro et al. Genome biology, 2020. ↗CC BY