A Bayesian approach to differential prevalence analysis with applications in microbiome studies

2026-02-05

AI Summary

This study addresses the limitations of conventional methods in microbial differential prevalence analysis, which are prone to boundary effects and multiple testing issues when handling sparse presence–absence data. To overcome these challenges, the authors propose DiPPER, a Bayesian hierarchical modeling framework specifically designed for differential prevalence inference. DiPPER represents the first application of Bayesian hierarchical models to this task, inherently incorporating multiple testing correction and providing principled uncertainty quantification. Systematic evaluation across 67 publicly available human gut microbiome datasets demonstrates that DiPPER effectively controls false discovery rates while maintaining high sensitivity, significantly outperforming existing methods for both differential prevalence and abundance analysis. Moreover, DiPPER exhibits markedly improved cross-study reproducibility, highlighting its robustness and reliability in real-world microbiome research.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Recent evidence suggests that analyzing the presence/absence of taxonomic features can offer a compelling alternative to differential abundance analysis in microbiome studies. However, standard approaches face challenges with boundary cases and multiple testing. To address these challenges, we developed DiPPER (Differential Prevalence via Probabilistic Estimation in R), a method based on Bayesian hierarchical modeling. We benchmarked our method against existing differential prevalence and abundance methods using data from 67 publicly available human gut microbiome studies. We observed considerable variation in performance across methods, with DiPPER outperforming alternatives by combining high sensitivity with effective error control. DiPPER also demonstrated superior replication of findings across independent studies. Furthermore, DiPPER provides differential prevalence estimates and uncertainty intervals that are inherently adjusted for multiple testing.

Problem

• differential prevalence
• microbiome studies
• boundary cases
• multiple testing
• presence/absence analysis

Innovation

• Bayesian hierarchical modeling
• differential prevalence
• microbiome studies
• multiple testing correction
• DiPPER