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

Autor:	Rahman G; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA., Morton JT; Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, USA., Martino C; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA., Sepich-Poore GD; Micronoma, San Diego, CA, USA., Allaband C; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA., Guccione C; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA., Chen Y; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.; Department of Dermatology, University of California San Diego, La Jolla, CA, USA.; Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA., Hakim D; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA., Estaki M; Department of Physiology & Pharmacology, University of Calgary, Calgary, Canada., Knight R; Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, California, USA.
Jazyk:	angličtina
Zdroj:	BioRxiv : the preprint server for biology [bioRxiv] 2023 Feb 02. Date of Electronic Publication: 2023 Feb 02.
DOI:	10.1101/2023.01.30.526328
Abstrakt:	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 ( B ayesian I nferential R egression for D ifferential M icrobiome An 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. Competing Interests: Conflicts of interest G.D.S.-P. and R.K. are inventors on a US patent application (PCT/US2019/059647) submitted by The Regents of the University of California and licensed by Micronoma; that application covers methods of diagnosing and treating cancer using multi-domain microbial biomarkers in blood and cancer tissues. G.D.S.-P. and R.K. are founders of and report stock interest in Micronoma. G.D.S.-P. has filed several additional US patent applications on cancer bacteriome and mycobiome diagnostics that are owned by The Regents of the University of California or Micronoma. R.K. additionally is a member of the scientific advisory board for GenCirq, holds an equity interest in GenCirq, and can receive reimbursements for expenses up to US $5,000 per year.
Databáze:	MEDLINE
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