ExplorerPharmaceutical ResearchBiochemistry
Research PaperResearchia:202605.12073

MeTime: An R package for reproducible longitudinal metabolomics data analysis

Bharadwaj Marella

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 t...

Submitted: May 12, 2026Subjects: Biochemistry; Pharmaceutical Research

Description / Details

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.


Source: arXiv:2605.08497v1 - http://arxiv.org/abs/2605.08497v1 PDF: https://arxiv.org/pdf/2605.08497v1 Original Link: http://arxiv.org/abs/2605.08497v1

Please sign in to join the discussion.

No comments yet. Be the first to share your thoughts!

Access Paper
View Source PDF
Submission Info
Date:
May 12, 2026
Topic:
Pharmaceutical Research
Area:
Biochemistry
Comments:
0
Bookmark