Bayesian chain graph models to characterize microbe-environment dynamics.

Area of Science:

• Microbial ecology
• Statistical modeling
• Bioinformatics

Background:

• Microbiome data analysis requires models that capture both environmental responses and microbe-microbe interactions.
• Standard multiresponse linear regression is insufficient for graphical modeling due to limitations in encoding conditional dependence structures.
• Prior biological knowledge, especially from experimental interventions, necessitates models that properly encode conditional dependence.

Purpose of the Study:

• To propose a novel chain graph model for microbiome data analysis.
• To develop a statistical framework that accurately represents conditional dependence between microbial responses and environmental predictors.
• To provide a computationally efficient and flexible model for inferring microbial network structures.

Main Methods:

• A chain graph model with distinct predictor and response node sets was developed.
• Bayesian linear regression with LASSO regularization was employed for sparse solutions.
• An adaptive extension allowed for edge-specific shrinkage, incorporating prior knowledge.
• A Gibbs sampling algorithm ensured computational efficiency.
• Hierarchical structures accommodated binary, counting, and compositional response types.

Main Results:

• The proposed model yields graphs where edges represent conditional dependencies, aligning with experimental intuition.
• The model demonstrated superior performance compared to state-of-the-art methods on simulated datasets.
• Application to human gut and soil microbiome data revealed biologically meaningful network structures.
• The CG-LASSO method effectively estimates microbial interaction networks.

Conclusions:

• The proposed chain graph model (CG-LASSO) provides a robust framework for microbiome network inference.
• It accurately captures conditional dependencies and integrates prior biological knowledge.
• The model offers a computationally efficient and flexible approach for analyzing complex microbial community data.