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

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 ...