MeTEor: an R Shiny app for exploring longitudinal metabolomics data

Gordon Grabert; Daniel Dehncke; Tushar More; Markus List; Anke R M Kraft; Markus Cornberg; Karsten Hiller; Tim Kacprowski

doi:10.1093/bioadv/vbae178

MeTEor: an R Shiny app for exploring longitudinal metabolomics data

Bioinform Adv. 2024 Nov 14;4(1):vbae178. doi: 10.1093/bioadv/vbae178. eCollection 2024.

Authors

Gordon Grabert^{1

2}, Daniel Dehncke^{1

2}, Tushar More^{2

3}, Markus List⁴, Anke R M Kraft^{5

6

7}, Markus Cornberg^{5

6

7}, Karsten Hiller^{2

3}, Tim Kacprowski^{1

2}

Affiliations

¹ Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany.
² Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany.
³ Department of Bioinformatics and Biochemistry, BRICS, Technische Universität Braunschweig, Braunschweig, Lower Saxony 38106, Germany.
⁴ Data Science in Systems Biology, School of Life Sciences, Technical University of Munich, Munich, Bavaria 85354, Germany.
⁵ Centre for Individualised Infection Medicine (CiiM), Joint Venture Between Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany.
⁶ Department of Gastroenterology, Hepatology, Infectious Diseases and Endocrinology, Hannover Medical School (MHH), Hannover, Lower Saxony 30625, Germany.
⁷ German Centre for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Hannover, Lower Saxony 38124, Germany.

Abstract

Motivation: The availability of longitudinal omics data is increasing in metabolomics research. Viewing metabolomics data over time provides detailed insight into biological processes and fosters understanding of how systems react over time. However, the analysis of longitudinal metabolomics data poses various challenges, both in terms of statistical evaluation and visualization.

Results: To make explorative analysis of longitudinal data readily available to researchers without formal background in computer science and programming, we present MEtabolite Trajectory ExplORer (MeTEor). MeTEor is an R Shiny app providing a comprehensive set of statistical analysis methods. To demonstrate the capabilities of MeTEor, we replicated the analysis of metabolomics data from a previously published study on COVID-19 patients.

Availability and implementation: MeTEor is available as an R package and as a Docker image. Source code and instructions for setting up the app can be found on GitHub (https://github.com/scibiome/meteor). The Docker image is available at Docker Hub (https://hub.docker.com/r/gordomics/meteor). MeTEor has been tested on Microsoft Windows, Unix/Linux, and macOS.