TY - JOUR
T1 - Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data
AU - Pakkir Shah, Abzer K.
AU - Walter, Axel
AU - Ottosson, Filip
AU - Russo, Francesco
AU - Navarro-Diaz, Marcelo
AU - Boldt, Judith
AU - Kalinski, Jarmo Charles J.
AU - Kontou, Eftychia Eva
AU - Elofson, James
AU - Polyzois, Alexandros
AU - González-Marín, Carolina
AU - Farrell, Shane
AU - Aggerbeck, Marie R.
AU - Pruksatrakul, Thapanee
AU - Chan, Nathan
AU - Wang, Yunshu
AU - Pöchhacker, Magdalena
AU - Brungs, Corinna
AU - Cámara, Beatriz
AU - Caraballo-Rodríguez, Andrés Mauricio
AU - Cumsille, Andres
AU - de Oliveira, Fernanda
AU - Dührkop, Kai
AU - El Abiead, Yasin
AU - Geibel, Christian
AU - Graves, Lana G.
AU - Hansen, Martin
AU - Heuckeroth, Steffen
AU - Knoblauch, Simon
AU - Kostenko, Anastasiia
AU - Kuijpers, Mirte C.M.
AU - Mildau, Kevin
AU - Papadopoulos Lambidis, Stilianos
AU - Portal Gomes, Paulo Wender
AU - Schramm, Tilman
AU - Steuer-Lodd, Karoline
AU - Stincone, Paolo
AU - Tayyab, Sibgha
AU - Vitale, Giovanni Andrea
AU - Wagner, Berenike C.
AU - Xing, Shipei
AU - Yazzie, Marquis T.
AU - Zuffa, Simone
AU - de Kruijff, Martinus
AU - Beemelmanns, Christine
AU - Link, Hannes
AU - Mayer, Christoph
AU - van der Hooft, Justin J.J.
AU - Damiani, Tito
AU - Pluskal, Tomáš
AU - Dorrestein, Pieter
AU - Stanstrup, Jan
AU - Schmid, Robin
AU - Wang, Mingxun
AU - Aron, Allegra
AU - Ernst, Madeleine
AU - Petras, Daniel
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
U2 - 10.1038/s41596-024-01046-3
DO - 10.1038/s41596-024-01046-3
M3 - Article
C2 - 39304763
AN - SCOPUS:85204703597
SN - 1754-2189
VL - 20
SP - 92
EP - 162
JO - Nature protocols
JF - Nature protocols
IS - 1
ER -