Multivariable association discovery in population-scale meta-omics studies
Fig 2
MaAsLin 2 controls false discovery rate while maintaining power in differential abundance analysis of microbial communities.
To assess models’ behaviors during differential abundance analysis, we simulated 100 independent datasets per parameter combination, each containing a single binary metadatum and a fixed number of true positive features (10% of features differentially abundant) for varying association strengths and sample sizes (S1A Fig). We then evaluated the ability of different microbiome association methods to recover these associations using a variety of performance metrics and summarized the results across runs. Both sensitivity and false discovery rates (FDR) are shown for the best-performing method from each class of models (as measured by average F1 score). Compared to zero-inflated and count-based approaches, MaAsLin 2’s linear model formulation consistently controlled false discovery rate at the intended nominal level while maintaining moderate sensitivity (full results in S1–S8 Data). Red line parallel to the x-axis is the target threshold for FDR in multiple testing. Methods are sorted by increasing order of average F1 score across all simulation parameters in this setting.