MeTime: An R package for reproducible longitudinal metabolomics data analysis

2026-05-08

AI Summary

This study addresses the lack of standardized workflows and reproducibility challenges in longitudinal metabolomics research by introducing metime_analyser, a modular analytical framework built on R’s S4 class system. The framework integrates diverse analytical methods—including PCA, UMAP, t-SNE, random forests, Boruta, WGCNA, linear mixed-effects models, generalized additive models, and partial correlation networks—within a unified interface, enabling seamless data transformation, computation, and meta-analysis. All intermediate results and provenance information are retained within containerized environments, ensuring transparency and traceability while supporting one-click generation of HTML or PDF reports. Accompanied by comprehensive documentation and illustrative case studies, metime_analyser substantially enhances analytical efficiency and the reproducibility of metabolomics investigations.

Abstract

MeTime is an opensource R package for reproducible analysis of longitudinal metabolomics data. It builds upon a central S4 container, metime_analyser, that stores multiple datasets, associated metadata and analysis outputs, enabling unified handling of complex longitudinal studies. Analyses are constructed by piping modular functions, beginning with data transformations (mod_), followed by calculations (calc_), and optional meta-analysis (meta_), so entire workflows remain transparent and easy to modify. MeTime wraps numerous existing methods within a consistent interface, including sample and metabolite distributions, correlation and distance matrices, dimensionality reduction (PCA, UMAP, tSNE), random forest imputation and feature selection via Boruta, eigenmetabolites and WGCNA based clustering, conservation index analysis, regression models (linear, mixed effects, and generalized additive), and partial correlation networks. By retaining all intermediate results and provenance within the container, MeTime facilitates iterative exploration and ensures reproducible reporting via automatically generated HTML and PDF outputs. Comprehensive user guides, case studies and reference documentation accompany the package, making MeTime a versatile platform for longitudinal omics workflows.

Problem

• longitudinal metabolomics
• reproducible analysis
• data integration
• workflow transparency
• omics data

Innovation

• longitudinal metabolomics
• reproducible analysis
• modular pipeline
• S4 container
• automated reporting