coda4microbiome

coda4microbiome performs compositional data analysis to identify predictive microbial signatures in cross-sectional and longitudinal microbiome studies.

Key Features:

• Compositional Data Handling: Handles the compositional structure of microbiome data using log-ratio-based approaches to avoid spurious results.
• Predictive Modeling: Identifies microbial signatures with high predictive power using log-ratios between pairs of microbial components while minimizing feature complexity.
• Penalized Regression Techniques: Employs penalized regression for variable selection within the all-pairs log-ratio model and extends this approach to longitudinal data by analyzing area under log-ratio trajectories.
• Microbial Signature Expression: Represents inferred signatures as weighted balances between two groups of taxa denoting positive and negative contributors.
• Graphical Representations: Provides graphical outputs to visualize and interpret identified microbial signatures and their dynamics.

Scientific Applications:

• Crohn's disease (cross-sectional): Applied to cross-sectional Crohn's disease datasets to identify microbial signatures associated with disease status.
• Infant microbiome development (longitudinal): Applied to longitudinal infant microbiome studies to infer dynamic microbial signatures by summarizing log-ratio trajectories.

Methodology:

Computational methods explicitly include pairwise log-ratio analysis (all-pairs log-ratio model), penalized regression for variable selection, summarization of longitudinal patterns via area under log-ratio trajectories, and representation of signatures as weighted balances between taxa.