MeTime: An R package for reproducible longitudinal metabolomics data analysis

TL;DR

MeTime is an open-source R package designed for reproducible, modular, and comprehensive analysis of longitudinal metabolomics data, integrating multiple methods and ensuring transparency and reproducibility.

Contribution

It introduces a unified, container-based workflow framework that wraps numerous existing methods for longitudinal metabolomics analysis in R.

Findings

Supports complex longitudinal study handling with a central data container.

Enables transparent workflows with automatic provenance and reporting.

Incorporates diverse analysis methods like PCA, WGCNA, and regression models.

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.