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Related Experiment Video

Updated: May 19, 2026

High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution

High-Throughput Image-Based Quantification of Mitochondrial DNA Synthesis and Distribution

Published on: May 5, 2023

MeTime: An R package for reproducible longitudinal metabolomics data analysis.

Bharadwaj Marella¹, Patrick Weinisch¹, Lara Vehovec¹

¹Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

|May 18, 2026

Summary

This summary is machine-generated.

MeTime is an open-source R package designed for reproducible longitudinal metabolomics data analysis. It offers a unified platform for complex studies, enhancing transparency and ease of modification in omics workflows.

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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.