MeTime: An R package for reproducible longitudinal metabolomics data analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Longitudinal metabolomics studies generate complex, high-dimensional data.
• Reproducible analysis is crucial for robust scientific discovery.
• Existing tools often lack unified frameworks for longitudinal data handling.

Purpose of the Study:

• To introduce MeTime, an open-source R package for reproducible longitudinal metabolomics analysis.
• To provide a versatile platform integrating diverse analytical methods within a consistent framework.
• To facilitate transparent, modifiable, and reproducible omics workflows.

Main Methods:

• Utilizes an S4 container (metime_analyser) for unified data and metadata management.
• Employs a modular, pipeable function structure (mod_, calc_, meta_) for transparent analysis construction.
• Integrates a wide array of established methods including imputation, feature selection, clustering, and network analysis.

Main Results:

• MeTime enables unified handling of multiple datasets and associated metadata.
• The package ensures transparency and ease of modification through its modular design.
• Retains intermediate results and provenance for iterative exploration and reproducible reporting.

Conclusions:

• MeTime provides a comprehensive and versatile platform for longitudinal omics data analysis.
• The package enhances reproducibility through integrated provenance tracking and automated reporting.
• Facilitates robust scientific discovery in longitudinal metabolomics research.