Abstract
Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
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Data availability
All the data and software reported in the paper are open source and freely available on GitHub and Zenodo repositories: https://github.com/Gscorreia89/chemometrics-tutorials, https://github.com/phenomecentre/metabotyping-dementia-urine and https://doi.org/10.5281/zenodo.4053166.
Code availability
All the data and software reported in the paper are open source and freely available on GitHub and Zenodo repositories: https://github.com/Gscorreia89/chemometrics-tutorials, https://github.com/phenomecentre/metabotyping-dementia-urine and https://doi.org/10.5281/zenodo.4053166.
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Acknowledgements
B.J.B. is supported by the Analytical Chemistry Trust Fund (Tom West Analytical Fellowship) and the Fondation Bettencourt Schueller. G.C. is supported by the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC). T.E. acknowledges support from the EU COSMOS project (grant agreement 312941), the EU PhenoMeNal project (Project reference: 654241), UK BBSRC grant BB/T007974/1 and NIH grant R01 HL133932-01. E.H. is supported by the Department of Jobs, Tourism, Science and Innovation, Government of Western Australian Government through the Premier’s Science Fellowship Program. This work was supported by the Medical Research Council (MRC) and National Institute for Health Research (NIHR) (grant number MC_PC_12025) and the MRC UK Consortium for MetAbolic Phenotyping (MAP/UK) (grant number MR/S010483/1). The Division of Systems Medicine is funded by grants from the MRC, BBSRC and NIHR, an Integrative Mammalian Biology (IMB) Capacity Building Award and an FP7- HEALTH- 2009- 241592 EuroCHIP grant and is supported by the NIHR Biomedical Research Centre Funding Scheme. The views expressed are those of the authors and not necessarily those of the (name of funder), the NHS, the NIHR or the Department of Health. AddNeuroMed was supported by the Innovative Medicines Initiative (IMI) Joint Undertaking under EMIF grant agreement, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. We thank the clinical leads for the consortium S. Lovestone (PI), H. Soininen, P. Mecocci, M. Tsolaki, B. Vellas and I. Kłoszewska for kind access to the data.
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Blaise, B.J., Correia, G.D.S., Haggart, G.A. et al. Statistical analysis in metabolic phenotyping. Nat Protoc 16, 4299–4326 (2021). https://doi.org/10.1038/s41596-021-00579-1
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DOI: https://doi.org/10.1038/s41596-021-00579-1
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