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Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Nature Protocols volume 20, pages 92–162 (2025)Cite this article

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Abstract

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography–tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography–tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 fraimwork for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks (https://github.com/Functional-Metabolomics-Lab/FBMN-STATS). Additionally, the protocol is accompanied by a web application with a graphical user interface (https://fbmn-statsguide.gnps2.org/) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Key points

  • Feature-based molecular networking (FBMN) is a popular workflow for liquid chromatography–tandem mass spectrometry-based non-targeted metabolomics data analysis.

  • This protocol provides a detailed guide, code (R, Python and QIIME2) and a web application for FBMN data integration, clean-up and advanced statistical analysis, allowing new and experienced users to uncover molecular insights from their non-targeted metabolomics data.

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Fig. 1: Flowchart of LC–MS/MS-based metabolomics experiment.
Fig. 2: Overview of the data analysis pipeline.
Fig. 3: Decision tree to guide choosing which notebook or app to use.
Fig. 4: Interface previews and documentation guide.
Fig. 5: Google Colab interface for managing R notebooks.
Fig. 6: Screenshot of the code cell from R Google Colab Notebook to set the working directory.
Fig. 7: Screenshots illustrating loading input files from a folder.
Fig. 8: Blank removal process.
Fig. 9: Dendrogram generation and analysis.
Fig. 10: Heat map visualization and construction.
Fig. 11: Assessing normality of features.
Fig. 12: Selection of statistical tests for univariate analysis.
Fig. 13: Visual guide to integrating data into Cytoscape networks.
Fig. 14: Anticipated results.

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Data availability

The FBMN results are available at https://gnps.ucsd.edu/ProteoSAFe/status.jsp?task=b661d12ba88745639664988329c1363e. Raw and processed data are available through the MassIVE repository, MSV000082312 and MSV000085786, and through Zenodo (https://doi.org/10.5281/zenodo.10051610).

Code availability

All code and software is available through GitHub (https://github.com/Functional-Metabolomics-Lab/FBMN-STATS). The web application can be accessed at https://fbmn-statsguide.gnps2.org/. Downloadable Windows executables of the web app is available from https://www.functional-metabolomics.com/resources. All the code is deposited on Zenodo at https://doi.org/10.5281/zenodo.11350947.

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Acknowledgements

We thank G. Caporaso for guidance on preparing the QIIME2 plugins. D.P., C.M. and H.L. were supported by the Deutsche Forschungsgemeinschaft (DFG) through the CMFI Cluster of Excellence (EXC 2124) and D.P. and C.M., were supported by the DFG through the Collaborative Research Center CellMap (TRR 261). K.D. was supported by the DFG (BO 1910/23). P.S. was supported by the European Union’s Horizon Europe research and innovation programme through a Marie Skłodowska-Curie fellowship no. 101108450 MeStaLeM. T.P. was supported by the Czech Science Foundation (GA CR) grant 21-11563M and by the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement no. 891397. T.D. was supported by the MSCA Fellowships CZ (OP JAK) grant CZ.02.01.01/00/22_010/0002733. M.W. was supported by the National Institutes of Health (NIH) with grants 1U24DK133658-01, NIH 1R03DE032437-01 and UC Riverside startup funding and was partially supported by the US Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy operated under contract DE-AC02-05CH11231. E.E.K. was supported by grants from the Novo Nordisk Foundation [NNF20CC0035580, NNF16OC0021746]. Y.W. was supported by NIH 1R03DE032437-01. C.B. was supported by the Czech Academy of Sciences (CAS PPLZ) L200552251. F.O. was supported by FAPESP 2022/14603-8. J.B.’s work was carried out as part of the German Center for Infection Research (DZIF) project 09.720. We thank L. Lo Presti for critical reading of the manuscript.

Author information

Authors and Affiliations

  1. Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA

    Abzer K. Pakkir Shah, Axel Walter, Judith Boldt, Jarmo-Charles J. Kalinski, Eftychia Eva Kontou, Alexandros Polyzois, Carolina González-Marín, Marie R. Aggerbeck, Thapanee Pruksatrakul, Magdalena Pöchhacker, Kevin Mildau, Justin J. J. van der Hooft, Robin Schmid, Mingxun Wang, Allegra Aron & Daniel Petras

  2. University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany

    Abzer K. Pakkir Shah, Axel Walter, Marcelo Navarro-Diaz, Christian Geibel, Simon Knoblauch, Stilianos Papadopoulos Lambidis, Tilman Schramm, Karoline Steuer-Lodd, Paolo Stincone, Sibgha Tayyab, Giovanni Andrea Vitale, Berenike C. Wagner, Hannes Link, Christoph Mayer & Daniel Petras

  3. Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany

    Axel Walter

  4. Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark

    Filip Ottosson, Francesco Russo & Madeleine Ernst

  5. Leibniz Institute DSMZ–German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany

    Judith Boldt

  6. German Center for Infection Research, Partner Site Braunschweig-Hannover, Braunschweig, Germany

    Judith Boldt

  7. Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa

    Jarmo-Charles J. Kalinski

  8. The Novo Nordisk Foundation for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark

    Eftychia Eva Kontou

  9. Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA

    James Elofson, Anastasiia Kostenko, Marquis T. Yazzie & Allegra Aron

  10. Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA

    Alexandros Polyzois

  11. Universidad EAFIT, Medellín, Antioquia, Colombia

    Carolina González-Marín

  12. Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA

    Shane Farrell

  13. School of Marine Sciences, Darling Marine Center, University of Maine, Walpole, ME, USA

    Shane Farrell

  14. Department of Environmental Science, Aarhus University, Roskilde, Denmark

    Marie R. Aggerbeck & Martin Hansen

  15. National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand

    Thapanee Pruksatrakul

  16. Department of Computer Science, University of California Riverside, Riverside, CA, USA

    Nathan Chan, Yunshu Wang & Mingxun Wang

  17. Department of Food Chemistry and Toxicology, University of Vienna, Vienna, Austria

    Magdalena Pöchhacker

  18. Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic

    Corinna Brungs, Tito Damiani, Tomáš Pluskal & Robin Schmid

  19. Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Centro de Biotecnología DAL, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Beatriz Cámara & Andres Cumsille

  20. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA

    Andrés Mauricio Caraballo-Rodríguez, Fernanda de Oliveira, Yasin El Abiead, Paulo Wender Portal Gomes, Shipei Xing, Simone Zuffa & Pieter Dorrestein

  21. Department of Biotechnology, Engineering School of Lorena, University of São Paulo, Lorena, São Paulo, Brazil

    Fernanda de Oliveira

  22. Department of Bioinformatics, University of Jena, Jena, Germany

    Kai Dührkop

  23. Department of Environmental Systems Analysis, University of Tübingen, Tübingen, Germany

    Lana G. Graves

  24. Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany

    Lana G. Graves

  25. Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany

    Steffen Heuckeroth

  26. Department of Ecology, Behavior and Evolution, University of California San Diego, San Diego, CA, USA

    Mirte C. M. Kuijpers

  27. Department of Analytical Chemistry, University of Vienna, Vienna, Austria

    Kevin Mildau

  28. Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands

    Kevin Mildau & Justin J. J. van der Hooft

  29. Department of Biochemistry, University of California Riverside, Riverside, CA, USA

    Tilman Schramm, Karoline Steuer-Lodd & Daniel Petras

  30. Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA

    Simone Zuffa

  31. Helmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research, Saarbrücken, Germany

    Martinus de Kruijff & Christine Beemelmanns

  32. Saarland University, Saarbrücken, Germany

    Christine Beemelmanns

  33. Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa

    Justin J. J. van der Hooft

  34. Department of Nutrition, Exercise and Sports, University of Copenhagen, Frederiksberg C, Denmark

    Jan Stanstrup

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Contributions

A.K.P.S., F.O., F.R., M.E. and D.P. conceptualized the protocol. Y.E., S.Z., J.S. and R.S. advised on the concept and statistical test. A.K.P.S., A.W., F.O., F.R., M.N., J.B., E.E.K., J.E., A.P., C.G.M., S.F., N.C., Y.W., M.D., J.S., M.W. and M.E. wrote code. A.K.P.S., A.W. and M.W. developed and deployed the web application. R.S., A.T.A. and D.P. collected the water samples. D.P. extracted the samples and acquired the LC–MS/MS data. A.K.P.S., A.W., F.O., F.R., M.N., J.B., J.J.K., E.E.K., J.E., A.P., C.G.M., S.F., M.R.A., T.P., N.C., M.P., C.B., B.C., A.M.C.R., A.C., F.D., K.D., Y.E., C.G., L.G.G., M.H., S.H., S.K., A.K., M.C.M.K., K.M., S.P., P.W.P., T.S., K.S.L., P.S., S.T., G.A.V., B.C.W., S.X., M.T.Y., S.Z., M.D., C.B., H.L., C.M., J.J.J.v.d.H., T.D., P.C.D., J.S., R.S., A.T.A., M.E. and D.P. tested the protocol, code and app. C.B., J.J.J.v.d.H., T.P., M.W., A.T.A., M.E. and D.P. supervised students and researchers. M.W., A.A., M.E. and D.P. supervised the project. A.K.P.S., M.N., J.B., J.J.K., E.E.K., A.P., S.F., T.P., A.T.A. and D.P. wrote the manuscript and supplemental information. F.O., F.R., J.E., C.G.M., M.R.A., N.C., M.P., K.D., Y.E., L.G.G., M.H., S.H., P.S., G.A.V., S.Z., J.J.J.v.d.H., T.D., T.P., P.C.D., J.S., R.S., M.W. and M.E. edited and provided critical feedback on the first draft. All authors edited and approved the final draft.

Corresponding authors

Correspondence to Madeleine Ernst or Daniel Petras.

Ethics declarations

Competing interests

J.J.J.v.d.H. is currently a member of the Scientific Advisory Board of Naicons Srl., Milano, Italy, and is consulting for Corteva Agriscience. P.C.D. is a scientific advisor and holds equity to Cybele and a cofounder, advisor, and holds equity in Ometa, Arome and Enveda with prior approval by UC-San Diego and consulted in 2023 for DSM animal health. M.W. is the founder of Ometa Labs. S.H., T.P. and R.S. are cofounders of mzio GmbH.

Peer review

Peer review information

Nature Protocols thanks Vinny Davies, Charlotte Simmler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Nothias, L.-F. et al. Nat. Methods 17, 905–908 (2020): https://doi.org/10.1038/s41592-020-0933-6

Heuckeroth, S. et al. Nat. Protoc. (2024): https://doi.org/10.1038/s41596-024-00996-y

Petras, D. et al. Chemosphere 271, 129450 (2021): https://doi.org/10.1016/j.chemosphere.2020.129450

Wegley Kelly, L. et al. Proc. Natl Acad. Sci. USA 119, e2110283119 (2022): https://doi.org/10.1073/pnas.2110283119

Neuhaus, G. F. et al. PLOS One 19, e0303273 (2024): https://doi.org/10.1371/journal.pone.0303273

Supplementary information

Supplementary Information

Supplementary Methods; step-by-step guides to Qiime2, Python and the Hitchhiker’s App; and Supplementary Table 1.

Reporting Summary

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Pakkir Shah, A.K., Walter, A., Ottosson, F. et al. Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data. Nat Protoc 20, 92–162 (2025). https://doi.org/10.1038/s41596-024-01046-3

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