BEEM-Static

BEEM-Static infers directed microbial interactions from cross-sectional microbiome profiling data using the generalized Lotka-Volterra (gLVM) model and is implemented as an R package to produce ecologically interpretable interaction networks.

Key Features:

• Implementation: Provided as an R package for application to microbiome profiling datasets.
• Model-Based Interaction Inference: Applies the generalized Lotka-Volterra (gLVM) framework to infer directed interactions among microbial taxa.
• Ecological Interpretability: Produces interaction parameters and outputs that enable ecologically meaningful interpretation of microbial relationships.
• Cross-sectional Data Analysis: Specifically tailored to infer interactions from single-time-point (cross-sectional) microbiome datasets.
• Interaction Network Output: Generates interaction networks representing predicted directed relationships among taxa.

Scientific Applications:

• Microbiome Research: Identifies potential keystone species and interaction patterns to study community dynamics and their implications for host health or environment.
• Systems Biology Insights: Provides systems-level interaction information for integrating microbial ecological relationships into broader biological models.

Methodology:

BEEM-Static fits the generalized Lotka-Volterra (gLVM) model to cross-sectional microbiome profiling data to infer directed interactions and outputs interaction networks for ecological analysis.