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

被引：8

作者：

Shah, Abzer K. Pakkir ^{[1
,2
]}

Walter, Axel ^{[1
,2
,3
]}

Ottosson, Filip ^{[4
]}

Russo, Francesco ^{[4
]}

Navarro-Diaz, Marcelo ^{[2
]}

Boldt, Judith ^{[1
,5
,6
]}

Kalinski, Jarmo-Charles J. ^{[1
,7
]}

Kontou, Eftychia Eva ^{[1
,8
]}

Elofson, James ^{[9
]}

Polyzois, Alexandros ^{[1
,10
,11
]}

Gonzalez-Marin, Carolina ^{[1
,12
]}

Farrell, Shane ^{[13
,14
]}

Aggerbeck, Marie R. ^{[1
,15
]}

Pruksatrakul, Thapanee ^{[1
,16
]}

Chan, Nathan ^{[17
]}

Wang, Yunshu ^{[17
]}

Poechhacker, Magdalena ^{[1
,18
]}

Brungs, Corinna ^{[19
]}

Camara, Beatriz ^{[20
]}

Caraballo-Rodriguez, Andres Mauricio ^{[21
]}

Cumsille, Andres ^{[20
]}

de Oliveira, Fernanda ^{[21
,22
]}

Duehrkop, Kai ^{[23
]}

El Abiead, Yasin ^{[21
]}

Geibel, Christian ^{[2
]}

Graves, Lana G. ^{[24
,25
]}

Hansen, Martin ^{[15
]}

Heuckeroth, Steffen ^{[26
]}

Knoblauch, Simon ^{[2
]}

Kostenko, Anastasiia ^{[9
]}

Kuijpers, Mirte C. M. ^{[27
]}

Mildau, Kevin ^{[1
,28
,29
]}

Papadopoulos Lambidis, Stilianos ^{[2
]}

Gomes, Paulo Wender Portal ^{[21
]}

Schramm, Tilman ^{[2
,30
]}

Steuer-Lodd, Karoline ^{[2
,30
]}

Stincone, Paolo ^{[2
]}

Tayyab, Sibgha ^{[2
]}

Vitale, Giovanni Andrea ^{[2
]}

Wagner, Berenike C. ^{[2
]}

Xing, Shipei ^{[21
]}

Yazzie, Marquis T. ^{[9
]}

Zuffa, Simone ^{[21
,31
]}

de Kruijff, Martinus ^{[32
]}

Beemelmanns, Christine ^{[32
,33
]}

Link, Hannes ^{[2
]}

Mayer, Christoph ^{[2
]}

van der Hooft, Justin J. J. ^{[1
,29
,34
]}

Damiani, Tito ^{[19
]}

Pluskal, Tomas ^{[19
]}

机构：

[1] Internet, Virtual Multi Lab, Riverside, CA 92507 USA

[2] Univ Tubingen, Interfac Inst Microbiol & Infect Med, Tubingen, Germany

[3] Univ Tubingen, Dept Comp Sci, Appl Bioinformat, Tubingen, Germany

[4] Statens Serum Inst, Danish Ctr Neonatal Screening, Dept Congenital Disorders, Sect Clin Mass Spectrometry, Copenhagen, Denmark

[5] Leibniz Inst DSMZ German Collect Microorganisms &, Braunschweig, Germany

[6] German Ctr Infect Res, Partner Site Braunschweig Hannover, Braunschweig, Germany

[7] Rhodes Univ, Dept Biochem & Microbiol, Makhanda, South Africa

[8] Tech Univ Denmark, Novo Nord Fdn Biosustainabil, Kongens Lyngby, Denmark

[9] Univ Denver, Dept Chem & Biochem, Denver, CO USA

[10] Cornell Univ, Boyce Thompson Inst, Ithaca, NY USA

[11] Cornell Univ, Dept Chem & Chem Biol, Ithaca, NY USA

[12] Univ EAFIT, Medellin, Antioquia, Colombia

[13] Bigelow Lab Ocean Sci, E Boothbay, ME USA

[14] Univ Maine, Darling Marine Ctr, Sch Marine Sci, Walpole, ME 04573 USA

[15] Aarhus Univ, Dept Environm Sci, Roskilde, Denmark

[16] Thailand Sci Pk, Natl Ctr Genet Engn & Biotechnol, Natl Sci & Technol Dev Agcy, Pathum Thani, Thailand

[17] Univ Calif Riverside, Dept Comp Sci, Riverside, CA USA

[18] Univ Vienna, Dept Food Chem & Toxicol, Vienna, Austria

[19] Czech Acad Sci, Inst Organ Chem & Biochem, Prague, Czech Republic

[20] Univ Tecn Federico Santa Maria, Ctr Biotecnol DAL, Lab Microbiol Mol & Biotecnol Ambiental, Valparaiso, Chile

[21] Univ Calif San Diego, Skaggs Sch Pharm & Pharmaceut Sci, San Diego, CA USA

[22] Univ Sao Paulo, Engn Sch Lorena, Dept Biotechnol, Lorena, SP, Brazil

[23] Univ Jena, Dept Bioinformat, Jena, Germany

[24] Univ Tubingen, Dept Environm Syst Anal, Tubingen, Germany

[25] Leibniz Inst Freshwater Ecol & Inland Fisheries, Berlin, Germany

[26] Univ Munster, Inst Inorgan & Analyt Chem, Munster, Germany

[27] Univ Calif San Diego, Dept Ecol Behav & Evolut, San Diego, CA USA

[28] Univ Vienna, Dept Analyt Chem, Vienna, Austria

[29] Wageningen Univ & Res, Bioinformat Grp, Wageningen, Netherlands

[30] Univ Calif Riverside, Dept Biochem, Riverside, CA 92521 USA

[31] Univ Calif San Diego, Collaborat Mass Spectrometry Innovat Ctr, Skaggs Sch Pharm & Pharmaceut Sci, San Diego, CA 92093 USA

[32] Helmholtz Inst Pharmaceut Res Saarland, Helmholtz Ctr Infect Res, Saarbrucken, Germany

[33] Saarland Univ, Saarbrucken, Germany

[34] Univ Johannesburg, Dept Biochem, Johannesburg, South Africa

[35] Univ Copenhagen, Dept Nutr Exercise & Sports, Frederiksberg, Denmark

来源：

NATURE PROTOCOLS | 2025年 / 20卷 / 01期

基金：

巴西圣保罗研究基金会; 美国国家卫生研究院;

关键词：

MASS-SPECTROMETRY DATA; CHROMATOGRAPHY/MASS SPECTROMETRY; UNTARGETED METABOLOMICS; MULTIVARIATE-ANALYSIS; DRIFT CORRECTION; GENOMIC DATA; R PACKAGE; GC-MS; DISCOVERY; NORMALIZATION;

D O I：

10.1038/s41596-024-01046-3

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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. 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. Feature-based molecular networking is used to analyze non-targeted liquid chromatography-tandem mass spectrometry metabolomics data. This protocol includes instructions, ready-made code and a web app (https://fbmn-statsguide.gnps2.org/) for statistical analysis of feature-based molecular networking results.

引用

页码：92 / 162

页数：74

共 39 条

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