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

ABSTRACT: 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 intro

SUBMITTER: Koslovsky MD 

PROVIDER: S-EPMC11869466 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature