A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms

Area of Science:

• Microbiology
• Statistical Modeling
• Bioinformatics

Background:

• Human microbiota composition influences health and disease.
• Microbial communities are often analyzed using phylogenetic trees.
• Existing models may not fully capture tree-structured count data.

Purpose of the Study:

• To develop a novel statistical model for analyzing multivariate, over-dispersed, tree-structured count data.
• To propose a penalized likelihood method for parameter estimation and covariate selection in regression models.
• To enhance prediction power in microbiota studies by incorporating phylogenetic information.

Main Methods:

• Extension of the Dirichlet multinomial distribution to a Dirichlet-tree multinomial distribution.
• Development of a Dirichlet-tree multinomial regression model.
• Application of a penalized likelihood method with accelerated proximal gradient optimization.

Main Results:

• Simulation studies demonstrated the effectiveness of the proposed statistical procedure.
• The Dirichlet-tree multinomial regression model accurately handles complex count data.
• Incorporating the phylogenetic tree structure significantly improved prediction accuracy.

Conclusions:

• The Dirichlet-tree multinomial regression model offers a powerful tool for analyzing microbial community data.
• This approach can advance personalized healthcare strategies and identify novel therapeutic drug targets.
• Accounting for evolutionary relationships is crucial for robust microbiota analysis and prediction.