Treffer: Streamlining feature elaboration and statistics analysis in metabolomics: the GetFeatistics R-package.
Title:
Streamlining feature elaboration and statistics analysis in metabolomics: the GetFeatistics R-package.
Authors:
Frigerio G; Center for Omics Sciences (COSR), IRCCS San Raffaele Scientific Institute, Milan, Italy.; Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg.; Department of Clinical Sciences and Community Health, University of Milan, and Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Source:
Journal of integrative bioinformatics [J Integr Bioinform] 2025 Dec 24. Date of Electronic Publication: 2025 Dec 24.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: De Gruyter Open Country of Publication: Germany NLM ID: 101503361 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1613-4516 (Electronic) Linking ISSN: 16134516 NLM ISO Abbreviation: J Integr Bioinform Subsets: MEDLINE
Imprint Name(s):
Publication: Berlin : De Gruyter Open
Original Publication: Bielefeld : IMBio e.V.
Original Publication: Bielefeld : IMBio e.V.
References:
Broadhurst, D, Goodacre, R, Reinke, SN, Kuligowski, J, Wilson, ID, Lewis, MR, et al.. Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 2018;14:72. https://doi.org/10.1007/s11306-018-1367-3 . (PMID: 10.1007/s11306-018-1367-3)
Kirwan, JA, Gika, H, Beger, RD, Bearden, D, Dunn, WB, Goodacre, R, et al.. Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management. Metabolomics 2022;18:70. https://doi.org/10.1007/s11306-022-01926-3 . (PMID: 10.1007/s11306-022-01926-3)
Frigerio, G, Moruzzi, C, Mercadante, R, Schymanski, EL, Fustinoni, S. Development and application of an LC-MS/MS untargeted exposomics method with a separated pooled quality control strategy. Molecules 2022;27. https://doi.org/10.3390/molecules27082580 . (PMID: 10.3390/molecules27082580)
Smith, CA, Want, EJ, O’Maille, G, Abagyan, R, Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 2006;78:779–87. https://doi.org/10.1021/ac051437y . (PMID: 10.1021/ac051437y)
Kuhl, C, Tautenhahn, R, Böttcher, C, Larson, TR, Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem 2012;84:283–9. https://doi.org/10.1021/ac202450g . (PMID: 10.1021/ac202450g)
Ruttkies, C, Schymanski, EL, Wolf, S, Hollender, J, Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminf 2016;8:3. https://doi.org/10.1186/s13321-016-0115-9 . (PMID: 10.1186/s13321-016-0115-9)
Schymanski, EL, Kondić, T, Neumann, S, Thiessen, PA, Zhang, J, Bolton, EE. Empowering large chemical knowledge bases for exposomics: pubchemlite meets MetFrag. J Cheminf 2021;13:1–15. https://doi.org/10.1186/s13321-021-00489-0 . (PMID: 10.1186/s13321-021-00489-0)
Helmus, R, ter Laak, TL, van Wezel, AP, de Voogt, P, Schymanski, EL. patRoon: open source software platform for environmental mass spectrometry based non-target screening. J Cheminf 2021;13:1–25. https://doi.org/10.1186/s13321-020-00477-w . (PMID: 10.1186/s13321-020-00477-w)
Helmus, R, van de Velde, B, Brunner, AM, ter Laak, TL, van Wezel, AP, Schymanski, EL. patRoon 2.0: improved non-target analysis workflows including automated transformation product screening. J Open Source Softw 2022;7:4029. https://doi.org/10.21105/joss.04029 . (PMID: 10.21105/joss.04029)
Tsugawa, H, Ikeda, K, Takahashi, M, Satoh, A, Mori, Y, Uchino, H, et al.. A lipidome atlas in MS-DIAL 4. Nat Biotechnol 2020;38:1159–63. https://doi.org/10.1038/s41587-020-0531-2 . (PMID: 10.1038/s41587-020-0531-2)
Matsuzawa, Y, Tsugawa, H, Matsuzawa, Y, Nishida, K, Takahashi, M, Buyantogtokh, B. MS-DIAL metabolomics MSP spectral kit containing EI-MS, MS/MS, and CCS values – last edited in Aug. 8th, 2024 [Internet]; 2024. https://systemsomicslab.github.io/compms/msdial/main.html#MSP [Accessed 8 Jan 2025].
Carnoli, AJ, Lohuis, PO, Buydens, LMC, Tinnevelt, GH, Jansen, JJ. Linear mixed-effects models in chemistry: a tutorial. Anal Chim Acta 2024;1304:342444. https://doi.org/10.1016/j.aca.2024.342444 . (PMID: 10.1016/j.aca.2024.342444)
Rigamonti, AE, Frigerio, G, Caroli, D, De Col, A, Cella, SG, Sartorio, A, et al.. A metabolomics-based investigation of the effects of a short-term body weight reduction program in a cohort of adolescents with obesity: a prospective interventional clinical study. Nutrients 2023;15:529. https://doi.org/10.3390/nu15030529 . (PMID: 10.3390/nu15030529)
McDonald, JF, Robert, AM. The uses of tobit analysis. Rev Econ Stat 1980;62:318–21.
Frigerio, G, Favero, C, Savino, D, Mercadante, R, Albetti, B, Dioni, L, et al.. Plasma metabolomic profiling in 1391 subjects with overweight and obesity from the SPHERE study. Metabolites 2021;11. https://doi.org/10.3390/metabo11040194 . (PMID: 10.3390/metabo11040194)
Wickham, H, Averick, M, Bryan, J, Chang, W, McGowan, LD, François, R, et al.. Welcome to the tidyverse. J Open Source Softw 2019;4:1686. https://doi.org/10.21105/joss.01686 . (PMID: 10.21105/joss.01686)
Wickham, H, Hesselberth, J, Salmon, M, Roy, O, Brüggemann, S. pkgdown: make static HTML documentation for a package [Internet]; 2024. Available from: https://CRAN.R-project.org/package=pkgdown .
Gu, H, Liu, G, Wang, J, Aubry, AF, Arnold, ME. Selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bioanalytical LC-MS/MS assays and impacts of using incorrect weighting factors on curve stability, data quality, and assay performance. Anal Chem 2014;86:8959–66. https://doi.org/10.1021/ac5018265 . (PMID: 10.1021/ac5018265)
Sonawane, SS, Chhajed, SS, Attar, SS, Kshirsagar, SJ. An approach to select linear regression model in bioanalytical method validation. J Anal Sci Technol 2019;10:1. https://doi.org/10.1186/s40543-018-0160-2 . (PMID: 10.1186/s40543-018-0160-2)
Schymanski, EL, Jeon, J, Gulde, R, Fenner, K, Ruff, M, Singer, HP, et al.. Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ Sci Technol 2014;48:2097–8. https://doi.org/10.1021/es5002105 . (PMID: 10.1021/es5002105)
Talavera, AB, Aurich, D, Aho, VTE, Singh, RR, Cheng, T, Zaslavsky, L, et al.. Studying the Parkinson’s disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study. Anal Bioanal Chem 2022;414:7399–419. https://doi.org/10.1007/s00216-022-04207-z . (PMID: 10.1007/s00216-022-04207-z)
Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 1995;57. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x . (PMID: 10.1111/j.2517-6161.1995.tb02031.x)
Kuznetsova, A, Brockhoff, PB, Christensen, RHB. lmerTest package: tests in linear mixed effects models. J Stat Software 2017;82:1–26. https://doi.org/10.18637/jss.v082.i13 . (PMID: 10.18637/jss.v082.i13)
Bates, D, Mächler, M, Bolker, B, Walker, S. Fitting linear mixed-effects models using lme4. J Stat Software 2015;67:1–48. https://doi.org/10.18637/jss.v067.i01 . (PMID: 10.18637/jss.v067.i01)
Kleiber, C, Zeileis, A. Applied econometrics with R [Internet]; 2008. Available from: https://CRAN.R-project.org/package=AER .
Kim, S, Thiessen, PA, Cheng, T, Yu, B, Bolton, EE. An update on PUG-REST: RESTful interface for programmatic access to PubChem. Nucleic Acids Res 2018;46:W563–70. https://doi.org/10.1093/nar/gky294 . (PMID: 10.1093/nar/gky294)
Djoumbou Feunang, Y, Eisner, R, Knox, C, Chepelev, L, Hastings, J, Owen, G, et al.. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminf 2016;8:61. https://doi.org/10.1186/s13321-016-0174-y . (PMID: 10.1186/s13321-016-0174-y)
Picart-Armada, S, Fernández-Albert, F, Vinaixa, M, Yanes, O, Perera-Lluna, A. FELLA: an R package to enrich metabolomics data. BMC Bioinf 2018;19:538. https://doi.org/10.1186/s12859-018-2487-5 . (PMID: 10.1186/s12859-018-2487-5)
Kirwan, JA, Gika, H, Beger, RD, Bearden, D, Dunn, WB, Goodacre, R, et al.. Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management. Metabolomics 2022;18:70. https://doi.org/10.1007/s11306-022-01926-3 . (PMID: 10.1007/s11306-022-01926-3)
Frigerio, G, Moruzzi, C, Mercadante, R, Schymanski, EL, Fustinoni, S. Development and application of an LC-MS/MS untargeted exposomics method with a separated pooled quality control strategy. Molecules 2022;27. https://doi.org/10.3390/molecules27082580 . (PMID: 10.3390/molecules27082580)
Smith, CA, Want, EJ, O’Maille, G, Abagyan, R, Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 2006;78:779–87. https://doi.org/10.1021/ac051437y . (PMID: 10.1021/ac051437y)
Kuhl, C, Tautenhahn, R, Böttcher, C, Larson, TR, Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem 2012;84:283–9. https://doi.org/10.1021/ac202450g . (PMID: 10.1021/ac202450g)
Ruttkies, C, Schymanski, EL, Wolf, S, Hollender, J, Neumann, S. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminf 2016;8:3. https://doi.org/10.1186/s13321-016-0115-9 . (PMID: 10.1186/s13321-016-0115-9)
Schymanski, EL, Kondić, T, Neumann, S, Thiessen, PA, Zhang, J, Bolton, EE. Empowering large chemical knowledge bases for exposomics: pubchemlite meets MetFrag. J Cheminf 2021;13:1–15. https://doi.org/10.1186/s13321-021-00489-0 . (PMID: 10.1186/s13321-021-00489-0)
Helmus, R, ter Laak, TL, van Wezel, AP, de Voogt, P, Schymanski, EL. patRoon: open source software platform for environmental mass spectrometry based non-target screening. J Cheminf 2021;13:1–25. https://doi.org/10.1186/s13321-020-00477-w . (PMID: 10.1186/s13321-020-00477-w)
Helmus, R, van de Velde, B, Brunner, AM, ter Laak, TL, van Wezel, AP, Schymanski, EL. patRoon 2.0: improved non-target analysis workflows including automated transformation product screening. J Open Source Softw 2022;7:4029. https://doi.org/10.21105/joss.04029 . (PMID: 10.21105/joss.04029)
Tsugawa, H, Ikeda, K, Takahashi, M, Satoh, A, Mori, Y, Uchino, H, et al.. A lipidome atlas in MS-DIAL 4. Nat Biotechnol 2020;38:1159–63. https://doi.org/10.1038/s41587-020-0531-2 . (PMID: 10.1038/s41587-020-0531-2)
Matsuzawa, Y, Tsugawa, H, Matsuzawa, Y, Nishida, K, Takahashi, M, Buyantogtokh, B. MS-DIAL metabolomics MSP spectral kit containing EI-MS, MS/MS, and CCS values – last edited in Aug. 8th, 2024 [Internet]; 2024. https://systemsomicslab.github.io/compms/msdial/main.html#MSP [Accessed 8 Jan 2025].
Carnoli, AJ, Lohuis, PO, Buydens, LMC, Tinnevelt, GH, Jansen, JJ. Linear mixed-effects models in chemistry: a tutorial. Anal Chim Acta 2024;1304:342444. https://doi.org/10.1016/j.aca.2024.342444 . (PMID: 10.1016/j.aca.2024.342444)
Rigamonti, AE, Frigerio, G, Caroli, D, De Col, A, Cella, SG, Sartorio, A, et al.. A metabolomics-based investigation of the effects of a short-term body weight reduction program in a cohort of adolescents with obesity: a prospective interventional clinical study. Nutrients 2023;15:529. https://doi.org/10.3390/nu15030529 . (PMID: 10.3390/nu15030529)
McDonald, JF, Robert, AM. The uses of tobit analysis. Rev Econ Stat 1980;62:318–21.
Frigerio, G, Favero, C, Savino, D, Mercadante, R, Albetti, B, Dioni, L, et al.. Plasma metabolomic profiling in 1391 subjects with overweight and obesity from the SPHERE study. Metabolites 2021;11. https://doi.org/10.3390/metabo11040194 . (PMID: 10.3390/metabo11040194)
Wickham, H, Averick, M, Bryan, J, Chang, W, McGowan, LD, François, R, et al.. Welcome to the tidyverse. J Open Source Softw 2019;4:1686. https://doi.org/10.21105/joss.01686 . (PMID: 10.21105/joss.01686)
Wickham, H, Hesselberth, J, Salmon, M, Roy, O, Brüggemann, S. pkgdown: make static HTML documentation for a package [Internet]; 2024. Available from: https://CRAN.R-project.org/package=pkgdown .
Gu, H, Liu, G, Wang, J, Aubry, AF, Arnold, ME. Selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bioanalytical LC-MS/MS assays and impacts of using incorrect weighting factors on curve stability, data quality, and assay performance. Anal Chem 2014;86:8959–66. https://doi.org/10.1021/ac5018265 . (PMID: 10.1021/ac5018265)
Sonawane, SS, Chhajed, SS, Attar, SS, Kshirsagar, SJ. An approach to select linear regression model in bioanalytical method validation. J Anal Sci Technol 2019;10:1. https://doi.org/10.1186/s40543-018-0160-2 . (PMID: 10.1186/s40543-018-0160-2)
Schymanski, EL, Jeon, J, Gulde, R, Fenner, K, Ruff, M, Singer, HP, et al.. Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ Sci Technol 2014;48:2097–8. https://doi.org/10.1021/es5002105 . (PMID: 10.1021/es5002105)
Talavera, AB, Aurich, D, Aho, VTE, Singh, RR, Cheng, T, Zaslavsky, L, et al.. Studying the Parkinson’s disease metabolome and exposome in biological samples through different analytical and cheminformatics approaches: a pilot study. Anal Bioanal Chem 2022;414:7399–419. https://doi.org/10.1007/s00216-022-04207-z . (PMID: 10.1007/s00216-022-04207-z)
Benjamini, Y, Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 1995;57. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x . (PMID: 10.1111/j.2517-6161.1995.tb02031.x)
Kuznetsova, A, Brockhoff, PB, Christensen, RHB. lmerTest package: tests in linear mixed effects models. J Stat Software 2017;82:1–26. https://doi.org/10.18637/jss.v082.i13 . (PMID: 10.18637/jss.v082.i13)
Bates, D, Mächler, M, Bolker, B, Walker, S. Fitting linear mixed-effects models using lme4. J Stat Software 2015;67:1–48. https://doi.org/10.18637/jss.v067.i01 . (PMID: 10.18637/jss.v067.i01)
Kleiber, C, Zeileis, A. Applied econometrics with R [Internet]; 2008. Available from: https://CRAN.R-project.org/package=AER .
Kim, S, Thiessen, PA, Cheng, T, Yu, B, Bolton, EE. An update on PUG-REST: RESTful interface for programmatic access to PubChem. Nucleic Acids Res 2018;46:W563–70. https://doi.org/10.1093/nar/gky294 . (PMID: 10.1093/nar/gky294)
Djoumbou Feunang, Y, Eisner, R, Knox, C, Chepelev, L, Hastings, J, Owen, G, et al.. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminf 2016;8:61. https://doi.org/10.1186/s13321-016-0174-y . (PMID: 10.1186/s13321-016-0174-y)
Picart-Armada, S, Fernández-Albert, F, Vinaixa, M, Yanes, O, Perera-Lluna, A. FELLA: an R package to enrich metabolomics data. BMC Bioinf 2018;19:538. https://doi.org/10.1186/s12859-018-2487-5 . (PMID: 10.1186/s12859-018-2487-5)
Contributed Indexing:
Keywords: computational metabolomics; data preprocessing; metabolomics workflow; non-targeted metabolomics; open-source software
Entry Date(s):
Date Created: 20251222 Latest Revision: 20251222
Update Code:
20251223
DOI:
10.1515/jib-2025-0047
PMID:
41429694
Database:
MEDLINE
Weitere Informationen
Metabolomics studies require complex data processing pipelines to ensure data quality and extract meaningful biological insights. GetFeatistics is an R-package developed to streamline the elaboration and statistical analysis of metabolomics data. For targeted analyses, the package enables calibration curve-based quantification with different data weighting options. For untargeted studies, it includes dedicated functions to import feature tables from tools like patRoon and MS-DIAL, assign annotation confidence levels, and filter features based on pooled quality control (QC) criteria, including options for group-specific pooled QCs. The package also provides functions for univariate and multivariate statistical analyses, notably streamlined regression modelling with fixed effects, mixed-effects models for longitudinal data, and Tobit regression for censoring values exceeding the limits of detection. Output tables are concise and informative, facilitating interpretation and reporting, while output visualisations are fully customisable via the ggplot grammar. Additional functionalities include automated retrieval of chemical properties from PubChem, ontology classification via ClassyFire, and pathway enrichment analysis using the FELLA package. GetFeatistics is publicly available on GitHub, with comprehensive documentation and a step-by-step vignette. By integrating key steps of the metabolomics workflow, the package aims to facilitate both exploratory studies and large-scale epidemiological applications in metabolomics research.
(© 2025 the author(s), published by De Gruyter, Berlin/Boston.)