Locally sparse varying coefficient mixed model with application to longitudinal microbiome differential abundance

TL;DR

This paper introduces a novel statistical model for longitudinal microbiome data that identifies time-specific differences in microbial abundance, accounting for temporal dependence and irregular sampling, leading to new biological insights.

Contribution

The paper presents a new varying coefficient mixed-effects model with local sparsity for longitudinal microbiome data analysis, accommodating irregular sampling and serial correlation.

Findings

Effective identification of time intervals with significant group differences.

Model performs well in simulations with dependence and missing data.

Application reveals novel biological insights in microbiome development.

Abstract

Differential abundance (DA) analysis in microbiome studies has recently been used to uncover a plethora of associations between microbial composition and various health conditions. While current approaches to DA typically apply only to cross-sectional data, many studies feature a longitudinal design to better understand the underlying microbial dynamics. To study DA in longitudinal microbial studies, we introduce a novel varying coefficient mixed-effects model with local sparsity. The proposed method can identify time intervals of significant group differences while accounting for temporal dependence. Specifically, we exploit a penalized kernel smoothing approach for parameter estimation and include a random effect to account for serial correlation. In particular, our method operates effectively regardless of whether sampling times are shared across subjects, accommodating irregular sampling and missing observations. Simulation studies demonstrate the necessity of modeling dependence for precise estimation and support recovery. The application of our method to a longitudinal study of mice oral microbiome during cancer development revealed significant scientific insights that were otherwise not discernible through cross-sectional analyses. An R implementation is available at https://github.com/fontaine618/LSVCMM.