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Marella, B. ; Weinisch, P. ; Vehovec, L. ; Tran, V. ; Bless, J.J. ; Njipouombe Nsangou, Y.A. ; Kastenmüller, G. ; Arnold, M.

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

Bioinfo. Adv. 6:vbag252 (2026)
Publ. Version/Full Text Research data DOI
Open Access Gold
Creative Commons Lizenzvertrag
Summary: MeTime is an opensource R package for reproducible analysis of longitudinal metabolomics data. It builds upona central S4 container, metime_analyser, that stores multiple datasets, associated metadata and analysis outputs, en-abling unified handling of complex longitudinal studies. Analyses are constructed by piping modular functions, beginningwith data transformations (mod_*), followed by calculations (calc_*), and optional meta-analysis (meta_*), so entireworkflows remain transparent and easy to modify. MeTime wraps numerous existing methods within a consistent inter-face, including sample and metabolite distributions, correlation/distance matrices, dimensionality reduction (PCA, UMAP,t-SNE), random forest imputation and feature selection via Boruta, eigenmetabolites and WGCNA-based clustering, con-servation index analysis, regression models (linear, mixed-effects, and generalized additive), and partial-correlation net-works. By retaining all intermediate results and provenance within the container, MeTime facilitates iterative explorationand ensures reproducible reporting via automatically generated HTML/PDF outputs. Comprehensive user guides, casestudies and reference documentation accompany the package, making MeTime a versatile platform for longitudinalomics workflows.
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Publication type Article: Journal article
Document type Review
Keywords R Package ; Dimensionality Reduction ; Workflow ; Modular Design ; Feature Selection ; Curse Of Dimensionality ; Imputation (statistics) ; Regression ; Metadata
ISSN (print) / ISBN 2635-0041
e-ISSN 2635-0041
Journal Bioinformatics Advances
Quellenangaben Volume: 6, Issue: 1, Pages: , Article Number: vbag252 Supplement: ,
Publisher Oxford University Press
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)