Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data
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| Názov: | Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data |
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| Autori: | Abzer K. Pakkir Shah, Axel Walter, Filip Ottosson, Francesco Russo, Marcelo Navarro-Diaz, Judith Boldt, Jarmo-Charles J. Kalinski, Eftychia Eva Kontou, James Elofson, Alexandros Polyzois, Carolina González-Marín, Shane Farrell, Marie R. Aggerbeck, Thapanee Pruksatrakul, Nathan Chan, Yunshu Wang, Magdalena Pöchhacker, Corinna Brungs, Beatriz Cámara, Andrés Mauricio Caraballo-Rodríguez, Andres Cumsille, Fernanda de Oliveira, Kai Dührkop, Yasin El Abiead, Christian Geibel, Lana G. Graves, Martin Hansen, Steffen Heuckeroth, Simon Knoblauch, Anastasiia Kostenko, Mirte C. M. Kuijpers, Kevin Mildau, Stilianos Papadopoulos Lambidis, Paulo Wender Portal Gomes, Tilman Schramm, Karoline Steuer-Lodd, Paolo Stincone, Sibgha Tayyab, Giovanni Andrea Vitale, Berenike C. Wagner, Shipei Xing, Marquis T. Yazzie, Simone Zuffa, Martinus de Kruijff, Christine Beemelmanns, Hannes Link, Christoph Mayer, Justin J. J. van der Hooft, Tito Damiani, Tomáš Pluskal, Pieter Dorrestein, Jan Stanstrup, Robin Schmid, Mingxun Wang, Allegra Aron, Madeleine Ernst, Daniel Petras |
| Zdroj: | VMOL 2025, 'Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data', Nature Protocols, vol. 20, no. 1, 103, pp. 92–162. https://doi.org/10.1038/s41596-024-01046-3 Nature Protocols |
| Informácie o vydavateľovi: | Springer Science and Business Media LLC, 2024. |
| Rok vydania: | 2024 |
| Predmety: | untargeted metabolomics, 104027 Computational chemistry, chromatography/mass spectrometry, 106005 Bioinformatik, mass spectrometry data, 106057 Metabolomics, 106057 Metabolomik, Life Science, 106005 Bioinformatics, 104027 Computational Chemistry |
| Popis: | 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 framework 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. |
| Druh dokumentu: | Article |
| Jazyk: | English |
| ISSN: | 1750-2799 1754-2189 |
| DOI: | 10.1038/s41596-024-01046-3 |
| Prístupová URL adresa: | https://pubmed.ncbi.nlm.nih.gov/39304763 https://ucrisportal.univie.ac.at/de/publications/9bb2e0cc-f091-47ba-a210-f4301c388c37 https://doi.org/10.1038/s41596-024-01046-3 https://hdl.handle.net/11104/0356179 https://research.wur.nl/en/publications/statistical-analysis-of-feature-based-molecular-networking-result https://doi.org/10.1038/s41596-024-01046-3 http://www.scopus.com/inward/record.url?scp=85204703597&partnerID=8YFLogxK https://pure.au.dk/portal/en/publications/c409dc47-4658-4015-b3f7-1d269a556ae8 https://doi.org/10.1038/s41596-024-01046-3 |
| Rights: | Springer Nature TDM |
| Prístupové číslo: | edsair.doi.dedup.....3cd5f881a308c392929e97b7786f723a |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | 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 framework 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. |
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| ISSN: | 17502799 17542189 |
| DOI: | 10.1038/s41596-024-01046-3 |