A 3-hour hands-on training in metabolomics data analysis, taught by Christina Schmidt and Denes Turei (Saez-Rodriguez group, Heidelberg University) at EMBL-EBI in May 2026.
The session is split into a 45-minute introductory talk followed by ~2 hours
of hands-on work. The hands-on portion uses R first (OmnipathR + MetaProViz)
and then Python (omnipath-client, omnipath-metabo).
notebooks/metabo_R.ipynb— the R notebook (IRkernel)notebooks/metabo_python.ipynb— the Python notebookscripts/R/— the R notebook's content as cell-marked scripts you can run section by section in any IDEscripts/python/— same idea for the Python contentdocs/— participant-facing setup, agenda, and glossaryenv/— environment definitions (pyproject.tomlfor Python,install.Rfor R)data/— small example data; the main toy dataset is shipped inside MetaProViz (MetaProViz::intracell_raw_se)2025/— last year's notebook, kept as historical referencetrainer/— internal scripts used by trainers; participants can ignore
Pick the path that fits your environment in docs/00_setup.md. The short
version:
cd env
uv sync
uv run jupyter labRscript env/install.RIf your machine cannot reach the Posit Public Package Manager, fall back to
the pre-built R library tarball — see docs/01_setup_r.md.
| min | content |
|---|---|
| 0–45 | Introductory talk (slides, no code) |
| 45–50 | Hands-on setup verification |
| 50–105 | R: data → processing → DMA → ID translation → PK → ORA |
| 105–110 | Network visualization (viz_graph) |
| 110–115 | Stretch + switch to Python |
| 115–155 | Python: omnipath-client + omnipath-metabo tour |
| 155–160 | (optional) BRENDA allosteric regulation |
| 160–180 | Q&A |
The detailed timetable lives in docs/04_agenda.md.
BSD 3-Clause — see LICENSE.
- MetaProViz — Schmidt C, Türei D, Prymidis D, Daley M, Frezza C, Saez-Rodriguez J. MetaProViz: a comprehensive R toolbox for metabolomics data analysis and visualisation. bioRxiv 2025. doi:10.1101/2025.08.18.670781
- OmniPath (latest) — Türei D, Schaul J, Palacio-Escat N, Bohár B, Bai Y, Ceccarelli F, et al. OmniPath: integrated knowledgebase for multi-omics analysis. Nucleic Acids Research, 54(D1):D652–D660, 2026. doi:10.1093/nar/gkaf1126
- OmniPath (original) — Türei D, Valdeolivas A, Gul L, et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol., 17:e9923, 2021. doi:10.15252/msb.20209923
- COSMOS (original) — Dugourd A, Kuppe C, Sciacovelli M, et al. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol. Syst. Biol., 17(1):e9730, 2021. doi:10.15252/msb.20209730
- COSMOS (latest preprint) — Modeling causal signal propagation in multi-omic factor space with COSMOS. bioRxiv 2024. doi:10.1101/2024.07.15.603538
- MetalinksDB — Farr E, Dimitrov D, Schmidt C, Türei D, Lobentanzer S, Dugourd A, Saez-Rodriguez J. MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions. Briefings in Bioinformatics, 25(4):bbae347, 2024. doi:10.1093/bib/bbae347
Materials and software developed by the Saez Lab (Heidelberg University Hospital), the Frezza Lab (CECAD, University of Cologne), the Korcsmaros Lab (Imperial College London), and the OmniPath Team.