Microbiome Toolbox

Microbiome Toolbox analyzes longitudinal gut microbiome composition and dynamics to quantify temporal changes and influences such as age, diet, and medication.

Key Features:

• Data Analysis and Exploration: Provides visualizations and reporting for complex longitudinal microbiome datasets.
• Data Preparation: Includes dataset-specific preprocessing and transformation capabilities.
• Feature Selection: Implements selection of optimal features for log-ratio denominators.
• Two-Group Analysis: Supports comparative analyses between two distinct groups within microbiome datasets.
• Microbiome Trajectory Prediction: Predicts microbiome trajectories over time and reports feature importance.
• Statistical Analysis: Performs spline and linear regression analyses to test universality across groups and differentiate trajectories.
• Longitudinal Anomaly Detection: Identifies anomalies within microbiome trajectories.
• Simulated Intervention: Simulates interventions aimed at returning anomalous trajectories to a reference state.
• Modularity: Provides a modular architecture to extend functionality with custom methods and analyses.

Scientific Applications:

• Longitudinal microbiome dynamics: Analyze temporal changes in gut microbiome composition and structure.
• Environmental and clinical factor effects: Quantify effects of age, diet, medication, and other factors on microbiome composition and function over time.
• Microbial ecology and health associations: Investigate microbial ecology and associations with health and disease.
• Intervention evaluation: Test simulated interventions to assess recovery of anomalous trajectories toward reference states.

Methodology:

Implemented in Python; dataset-specific preprocessing and transformations; feature selection for log-ratio denominators; two-group comparative analyses; trajectory prediction with feature importance; spline and linear regression analyses; longitudinal anomaly detection; simulated intervention.