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

TL;DR

This paper introduces DiPER, a Bayesian hierarchical model for differential prevalence analysis in microbiome studies, which improves detection accuracy, controls errors, and replicates findings better than existing methods.

Contribution

The paper presents DiPER, a novel Bayesian method for differential prevalence analysis that addresses boundary issues and multiple testing, outperforming existing approaches.

Findings

DiPER outperforms existing methods in sensitivity and error control.

DiPER shows superior replication of results across studies.

DiPER provides inherently adjusted uncertainty intervals.

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.