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

2026-01-15

AI Summary

This study addresses the limitations of existing differential abundance analysis methods, which are primarily designed for cross-sectional microbiome data and fail to capture the dynamic temporal patterns and time dependencies inherent in longitudinal studies. To overcome this, we propose a novel approach that integrates local sparsity with a varying-coefficient mixed-effects model. Our method employs penalized kernel smoothing to estimate time-varying coefficients, incorporates random effects to account for asynchronous, irregular sampling and missing observations across individuals, and leverages sparse regularization for effective variable selection. This is the first framework to jointly model temporal dynamics and group differences in longitudinal microbiome data, enabling precise identification of significant time windows of differential abundance. Simulations demonstrate superior performance in both parameter estimation and variable selection compared to methods that ignore dependency structures. Applied to a mouse oral microbiome study of cancer progression, our approach reveals critical dynamic patterns undetectable by cross-sectional analyses.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Kernel Methods
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• differential abundance
• longitudinal microbiome
• time-varying effects
• irregular sampling
• temporal dependence

Innovation

• varying coefficient model
• local sparsity
• longitudinal microbiome
• mixed-effects model
• penalized kernel smoothing