Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 69 - 19
• Main Author: Koslovsky, Matthew D.
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 27.02.2025; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Biology; Care and treatment; Child; Classification; Complications and side effects; Composition; Compositional; Data analysis; Gastrointestinal Microbiome; High-dimensional; Humans; Identification and classification; Inflation (Finance); Microbial activity; Microbiomes; Microbiota; Microbiota (Symbiotic organisms); Microorganisms; Modelling; Multivariate count data; Obesity; Obesity - microbiology; Obesity in children; Phenetics; Probability; Sequences; Sparsity; Taxonomy; United States; Obesity; High-dimensional; Multivariate count data; Compositional
• DOI: 10.1186/s12859-025-06078-4
• ISSN: 1471-2105

The human microbiome is the collection of microorganisms living on and inside of our bodies. A major aim of microbiome research is understanding the role microbial communities play in human health with the goal of designing personalized interventions that modulate the microbiome to treat or prevent disease. Microbiome data are challenging to analyze due to their high-dimensionality, overdispersion, and zero-inflation. Analysis is further complicated by the steps taken to collect and process microbiome samples. For example, sequencing instruments have a fixed capacity for the total number of reads delivered. It is therefore essential to treat microbial samples as compositional. Another complicating factor of modeling microbiome data is that taxa counts are subject to measurement error introduced at various stages of the measurement protocol. Advances in sequencing technology and preprocessing pipelines coupled with our growing knowledge of the human microbiome have reduced, but not eliminated, measurement error. Ignoring measurement error during analysis, though common in practice, can then lead to biased inference and curb reproducibility. We propose a Dirichlet-multinomial modeling framework for microbiome data with excess zeros and potential taxonomic misclassification. We demonstrate how accommodating taxonomic misclassification improves estimation performance and investigate differences in gut microbial composition between healthy and obese children.