The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data

Abzer K. Pakkir Shah; Axel Walter; F. Ottosson; Francesco Russo; Marcelo Navarro-Díaz; Judith Boldt; Jarmo-Charles Kalinski; Eftychia E. Kontou; James Elofson; Alexandros Polyzois; Carolina González-Marín; Shane Farrell; Marie Rønne 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; Heuckeroth Steffen; Simon Knoblauch; Anastasiia Kostenko; Mirte C.M. Kuijpers; K.A. Mildau; Stilianos Papadopoulos Lambidis; Paulo Wender P. 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; J.J.J. van der Hooft; Tito Damiani; Tomáš Pluskal; Pieter C. Dorrestein; Jan Stanstrup; Robin Schmid; Mingxun Wang; Allegra T. Aron; Madeleine Ernst; Daniel Petras

doi:10.26434/chemrxiv-2023-wwbt0

The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data

Abzer K. Pakkir Shah, Axel Walter, F. Ottosson, Francesco Russo, Marcelo Navarro-Díaz, Judith Boldt, Jarmo-Charles Kalinski, Eftychia E. Kontou, James Elofson, Alexandros Polyzois, Carolina González-Marín, Shane Farrell, Marie Rønne Aggerbeck, Thapanee Pruksatrakul, Nathan Chan, Yunshu Wang, Magdalena Pöchhacker, Corinna Brungs, Beatriz Cámara, Andrés Mauricio Caraballo-RodríguezAndres Cumsille, Fernanda de Oliveira, Kai Dührkop, Yasin El Abiead, Christian Geibel, Lana G. Graves, Martin Hansen, Heuckeroth Steffen, Simon Knoblauch, Anastasiia Kostenko, Mirte C.M. Kuijpers, K.A. Mildau, Stilianos Papadopoulos Lambidis, Paulo Wender P. 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, J.J.J. van der Hooft, Tito Damiani, Tomáš Pluskal, Pieter C. Dorrestein, Jan Stanstrup, Robin Schmid, Mingxun Wang, Allegra T. Aron, Madeleine Ernst, Daniel Petras

Bioinformatics

Research output: Working paper › Preprint

Abstract

Feature-Based Molecular Networking (FBMN) is a popular analysis approach for LC-MS/MS-based non-targeted metabolomics data. While processing LC-MS/MS data through FBMN is fairly streamlined, downstream data handling and statistical interrogation is often a key bottleneck. Especially, users new to statistical analysis struggle to effectively handle and analyze complex data matrices. In this protocol, we provide a comprehensive guide for the statistical analysis of FBMN results. We explain the data structure and principles of data clean-up 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. 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. Together, the protocol, code, and web app provide a complete guide and toolbox for FBMN data integration, clean-up, 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 (GNPS and GNPS2) and can be adapted to other MS feature detection, annotation, and networking tools.

Original language	English
Publisher	ChemRxiv
DOIs	https://doi.org/10.26434/chemrxiv-2023-wwbt0
Publication status	Published - 1 Nov 2023

Access to Document

10.26434/chemrxiv-2023-wwbt0Licence: CC BY-NC-ND

Cite this

Pakkir Shah, A. K., Walter, A., Ottosson, F., Russo, F., Navarro-Díaz, M., Boldt, J., Kalinski, J.-C., Kontou, E. E., Elofson, J., Polyzois, A., González-Marín, C., Farrell, S., Aggerbeck, M. R., Pruksatrakul, T., Chan, N., Wang, Y., Pöchhacker, M., Brungs, C., Cámara, B., ... Petras, D. (2023). The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data. ChemRxiv. https://doi.org/10.26434/chemrxiv-2023-wwbt0

@techreport{b4e2960744ff459bb8cbaf80d57e7c6c,

title = "The Hitchhiker{\textquoteright}s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data",

abstract = "Feature-Based Molecular Networking (FBMN) is a popular analysis approach for LC-MS/MS-based non-targeted metabolomics data. While processing LC-MS/MS data through FBMN is fairly streamlined, downstream data handling and statistical interrogation is often a key bottleneck. Especially, users new to statistical analysis struggle to effectively handle and analyze complex data matrices. In this protocol, we provide a comprehensive guide for the statistical analysis of FBMN results. We explain the data structure and principles of data clean-up 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. 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. Together, the protocol, code, and web app provide a complete guide and toolbox for FBMN data integration, clean-up, 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 (GNPS and GNPS2) and can be adapted to other MS feature detection, annotation, and networking tools. ",

author = "{Pakkir Shah}, {Abzer K.} and Axel Walter and F. Ottosson and Francesco Russo and Marcelo Navarro-D{\'i}az and Judith Boldt and Jarmo-Charles Kalinski and Kontou, {Eftychia E.} and James Elofson and Alexandros Polyzois and Carolina Gonz{\'a}lez-Mar{\'i}n and Shane Farrell and Aggerbeck, {Marie R{\o}nne} and Thapanee Pruksatrakul and Nathan Chan and Yunshu Wang and Magdalena P{\"o}chhacker and Corinna Brungs and Beatriz C{\'a}mara and Caraballo-Rodr{\'i}guez, {Andr{\'e}s Mauricio} and Andres Cumsille and {de Oliveira}, Fernanda and Kai D{\"u}hrkop and {El Abiead}, Yasin and Christian Geibel and Graves, {Lana G.} and Martin Hansen and Heuckeroth Steffen and Simon Knoblauch and Anastasiia Kostenko and Kuijpers, {Mirte C.M.} and K.A. Mildau and {Papadopoulos Lambidis}, Stilianos and Gomes, {Paulo Wender P.} and Tilman Schramm and Karoline Steuer-Lodd and Paolo Stincone and Sibgha Tayyab and Vitale, {Giovanni Andrea} and Wagner, {Berenike C.} and Shipei Xing and Yazzie, {Marquis T.} and Simone Zuffa and {de Kruijff}, Martinus and Christine Beemelmanns and Hannes Link and Christoph Mayer and {van der Hooft}, J.J.J. and Tito Damiani and Tom{\'a}{\v s} Pluskal and Dorrestein, {Pieter C.} and Jan Stanstrup and Robin Schmid and Mingxun Wang and Aron, {Allegra T.} and Madeleine Ernst and Daniel Petras",

year = "2023",

month = nov,

day = "1",

doi = "10.26434/chemrxiv-2023-wwbt0",

language = "English",

publisher = "ChemRxiv",

address = "United Kingdom",

type = "WorkingPaper",

institution = "ChemRxiv",

}

Pakkir Shah, AK, Walter, A, Ottosson, F, Russo, F, Navarro-Díaz, M, Boldt, J, Kalinski, J-C, Kontou, EE, Elofson, J, Polyzois, A, González-Marín, C, Farrell, S, Aggerbeck, MR, Pruksatrakul, T, Chan, N, Wang, Y, Pöchhacker, M, Brungs, C, Cámara, B, Caraballo-Rodríguez, AM, Cumsille, A, de Oliveira, F, Dührkop, K, El Abiead, Y, Geibel, C, Graves, LG, Hansen, M, Steffen, H, Knoblauch, S, Kostenko, A, Kuijpers, MCM, Mildau, KA, Papadopoulos Lambidis, S, Gomes, PWP, Schramm, T, Steuer-Lodd, K, Stincone, P, Tayyab, S, Vitale, GA, Wagner, BC, Xing, S, Yazzie, MT, Zuffa, S, de Kruijff, M, Beemelmanns, C, Link, H, Mayer, C, van der Hooft, JJJ, Damiani, T, Pluskal, T, Dorrestein, PC, Stanstrup, J, Schmid, R, Wang, M, Aron, AT, Ernst, M & Petras, D 2023 'The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data' ChemRxiv. https://doi.org/10.26434/chemrxiv-2023-wwbt0

TY - UNPB

T1 - The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data

AU - Pakkir Shah, Abzer K.

AU - Walter, Axel

AU - Ottosson, F.

AU - Russo, Francesco

AU - Navarro-Díaz, Marcelo

AU - Boldt, Judith

AU - Kalinski, Jarmo-Charles

AU - Kontou, Eftychia E.

AU - Elofson, James

AU - Polyzois, Alexandros

AU - González-Marín, Carolina

AU - Farrell, Shane

AU - Aggerbeck, Marie Rønne

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 - Steffen, Heuckeroth

AU - Knoblauch, Simon

AU - Kostenko, Anastasiia

AU - Kuijpers, Mirte C.M.

AU - Mildau, K.A.

AU - Papadopoulos Lambidis, Stilianos

AU - Gomes, Paulo Wender P.

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, J.J.J.

AU - Damiani, Tito

AU - Pluskal, Tomáš

AU - Dorrestein, Pieter C.

AU - Stanstrup, Jan

AU - Schmid, Robin

AU - Wang, Mingxun

AU - Aron, Allegra T.

AU - Ernst, Madeleine

AU - Petras, Daniel

PY - 2023/11/1

Y1 - 2023/11/1

N2 - Feature-Based Molecular Networking (FBMN) is a popular analysis approach for LC-MS/MS-based non-targeted metabolomics data. While processing LC-MS/MS data through FBMN is fairly streamlined, downstream data handling and statistical interrogation is often a key bottleneck. Especially, users new to statistical analysis struggle to effectively handle and analyze complex data matrices. In this protocol, we provide a comprehensive guide for the statistical analysis of FBMN results. We explain the data structure and principles of data clean-up 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. 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. Together, the protocol, code, and web app provide a complete guide and toolbox for FBMN data integration, clean-up, 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 (GNPS and GNPS2) and can be adapted to other MS feature detection, annotation, and networking tools.

AB - Feature-Based Molecular Networking (FBMN) is a popular analysis approach for LC-MS/MS-based non-targeted metabolomics data. While processing LC-MS/MS data through FBMN is fairly streamlined, downstream data handling and statistical interrogation is often a key bottleneck. Especially, users new to statistical analysis struggle to effectively handle and analyze complex data matrices. In this protocol, we provide a comprehensive guide for the statistical analysis of FBMN results. We explain the data structure and principles of data clean-up 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. 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. Together, the protocol, code, and web app provide a complete guide and toolbox for FBMN data integration, clean-up, 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 (GNPS and GNPS2) and can be adapted to other MS feature detection, annotation, and networking tools.

U2 - 10.26434/chemrxiv-2023-wwbt0

DO - 10.26434/chemrxiv-2023-wwbt0

M3 - Preprint

BT - The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data

PB - ChemRxiv

ER -

The Hitchhiker’s Guide to Statistical Analysis of Feature-based Molecular Networks from Non-Targeted Metabolomics Data

Abstract

Access to Document

Fingerprint

Cite this