DiPPER: A Bayesian approach to differential prevalence analysis with applications in microbiome studies
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 to differential prevalence analysis face challenges with boundary cases and multiple testing. To address these limitations, 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 methods, along with two differential abundance tools, using publicly available data from 57 human gut microbiome studies. We observed considerable variation in performance across the evaluated methods. Importantly, DiPPER demonstrated high sensitivity to detect potentially differentially prevalent features while maintaining a well-calibrated family-wise error rate under the global null hypothesis. Most notably, it outperformed the alternatives in the replication of findings across independent studies. Furthermore, DiPPER provides differential prevalence estimates and uncertainty intervals that are inherently adjusted for multiple testing.