BIRDMAn: A Bayesian differential abundance framework that enables robust inference of host-microbe associations

• Published in: bioRxiv
• Main Authors: Rahman, Gibraan, Morton, James T, Martino, Cameron, Sepich-Poore, Gregory D, Allaband, Celeste, Guccione, Caitlin, Chen, Yang, Hakim, Daniel, Estaki, Mehrbod, Knight, Rob
• Format: Journal Article Paper
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 02.02.2023; Cold Spring Harbor Laboratory
• Edition: 1.1
• Subjects: Bayesian analysis; Bioinformatics; Cancer; Genomes; Mathematical models; Microbiomes; Patent applications; United States > US; California
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2023.01.30.526328

Quantifying the differential abundance (DA) of specific taxa among experimental groups in microbiome studies is challenging due to data characteristics (e.g., compositionality, sparsity) and specific study designs (e.g., repeated measures, meta-analysis, cross-over). Here we present BIRDMAn ( ayesian nferential egression for ifferential icrobiome alysis), a flexible DA method that can account for microbiome data characteristics and diverse experimental designs. Simulations show that BIRDMAn models are robust to uneven sequencing depth and provide a >20-fold improvement in statistical power over existing methods. We then use BIRDMAn to identify antibiotic-mediated perturbations undetected by other DA methods due to subject-level heterogeneity. Finally, we demonstrate how BIRDMAn can construct state-of-the-art cancer-type classifiers using The Cancer Genome Atlas (TCGA) dataset, with substantial accuracy improvements over random forests and existing DA tools across multiple sequencing centers. Collectively, BIRDMAn extracts more informative biological signals while accounting for study-specific experimental conditions than existing approaches.