MeLSI: Metric Learning for Statistical Inference in microbiome community composition analysis.

Microbiome beta diversity analysis relies on distance-based methods, including permutational multivariate analysis of variance (PERMANOVA) combined with fixed ecological distance metrics (Bray-Curtis, Euclidean, Jaccard, and UniFrac), which treat all microbial taxa uniformly, regardless of their biological relevance to community differences. This "one-size-fits-all" approach may miss subtle but biologically meaningful patterns in complex microbiome data. We present Metric Learning for Statistical Inference (MeLSI), a novel machine learning framework that learns data-adaptive distance metrics optimized for detecting community composition differences in multivariate microbiome analyses. MeLSI employs an ensemble of weak learners using bootstrap sampling, feature subsampling, and gradient-based optimization to learn optimal feature weights, combined with rigorous permutation testing for statistical inference. The learned metrics can be used with PERMANOVA for hypothesis testing and with principal coordinates analysis for ordination visualization. Comprehensive validation on synthetic benchmarks and real data sets shows that MeLSI maintains proper type I error control while delivering competitive or superior statistical power for detecting subtle community shifts and, crucially, supplies interpretable feature-weight profiles that clarify which taxa drive group separation. On the DietSwap data set, MeLSI was the only method to achieve significance at α = 0.05, demonstrating that adaptive weighting can detect diet-induced community shifts that fixed metrics miss. Across all data sets, the learned feature weights identified biologically relevant taxa while providing actionable insight that no fixed distance metric can supply. MeLSI therefore offers a statistically rigorous tool that augments beta diversity analysis with transparent, data-driven interpretability.IMPORTANCEUnderstanding which microbes differ between groups of interest could reveal therapeutic targets and diagnostic biomarkers. However, current analysis methods treat all microbes equally (similar to using the same ruler to measure everything, regardless of what matters most). This means subtle but biologically important differences may go undetected, especially when only a few key species drive disease states while hundreds of "bystander" species add noise. Metric Learning for Statistical Inference (MeLSI) solves this by learning which microbes matter most for each specific comparison. In comparing male and female gut microbiomes, MeLSI identified specific bacterial families driving the differences, providing actionable biological insights that standard methods miss. This capability is particularly crucial for detecting early disease biomarkers, where differences are subtle and masked by biological variability. By telling researchers not just whether groups differ, but which specific microbes drive those differences, MeLSI accelerates the path from microbiome data to testable biological hypotheses and clinical applications.