MetaboDynamics

MetaboDynamics provides a framework of probabilistic models to analyze longitudinal metabolomics data, enabling robust estimation of mean concentrations despite varying spread between timepoints and reporting differences between timepoints as well as metabolite specific dynamics profiles.

Key Features:

• Robust Mean Estimation: Enables robust estimation of mean concentrations despite varying spread between timepoints.
• Dynamics Profiling: Reports metabolite specific dynamics profiles that can be used for identifying "dynamics clusters" of metabolites of similar dynamics.
• Probabilistic Over-representation Analysis: Provides probabilistic over-representation analysis of KEGG functional modules and pathways.
• Cluster Comparison: Supports comparison between clusters of different experimental conditions.

Scientific Applications:

• Longitudinal Metabolomics Analysis: Analyzes longitudinal metabolomics data with probabilistic models.
• Dynamics Cluster Identification: Identifies "dynamics clusters" of metabolites with similar temporal dynamics profiles.
• KEGG Pathway Analysis: Performs probabilistic over-representation analysis of KEGG functional modules and pathways.
• Experimental Condition Comparison: Compares metabolite dynamics clusters across different experimental conditions.

Methodology:

Bayesian probabilistic models for longitudinal metabolomics data analysis.