It's All Relative: New Regression Paradigm for Microbiome Compositional Data

TL;DR

This paper introduces a novel regression framework for microbiome compositional data that directly models compositions, improving interpretability and feature selection across taxonomic levels, with proven efficacy in simulations and real data.

Contribution

A new relative-shift regression paradigm for microbiome data that handles zeros and taxonomy, with regularization and efficient algorithms for feature selection and interpretation.

Findings

Effective in identifying relevant microbes at different taxonomic levels.

Outperforms existing methods in simulation studies.

Reveals novel microbiome-neurodevelopment associations.

Abstract

Microbiome data are complex in nature, involving high dimensionality, compositionally, zero inflation, and taxonomic hierarchy. Compositional data reside in a simplex that does not admit the standard Euclidean geometry. Most existing compositional regression methods rely on transformations that are inadequate or even inappropriate in modeling data with excessive zeros and taxonomic structure. We develop a novel relative-shift regression framework that directly uses compositions as predictors. The new framework provides a paradigm shift for compositional regression and offers a superior biological interpretation. New equi-sparsity and taxonomy-guided regularization methods and an efficient smoothing proximal gradient algorithm are developed to facilitate feature aggregation and dimension reduction in regression. As a result, the framework can automatically identify clinically relevant microbes even if they are important at different taxonomic levels. A unified finite-sample prediction error bound is developed for the proposed regularized estimators. We demonstrate the efficacy of the proposed methods in extensive simulation studies. The application to a preterm infant study reveals novel insights of association between the gut microbiome and neurodevelopment.