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Statistical analysis in metabolic phenotyping

Blaise, Benjamin J. (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Paediatric Anaesthetics, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom; Centre for the Developing Brain, King’s College London, London, United Kingdom
Correia, Gonçalo D. S. (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
Haggart, Gordon A. (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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Surowiec, Izabella (author)
Umeå universitet,Kemiska institutionen,Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umeå, Sweden
Sands, Caroline (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
Lewis, Matthew R. (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
Pearce, Jake T. M. (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; National Phenome Centre, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
Trygg, Johan (author)
Umeå universitet,Kemiska institutionen,Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umeå, Sweden
Nicholson, Jeremy K. (author)
Australian National Phenome Centre, Health Futures Institute, Murdoch University, WA, Perth, Australia; Institute of Global Health Innovation, Imperial College London, London, United Kingdom
Holmes, Elaine (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom; Centre for Computational & Systems Medicine Institute of Health Futures, Murdoch University, WA, Perth, Australia
Ebbels, Timothy M. D. (author)
Division of Systems Medicine, Department of Metabolism, Digestion & Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom
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 (creator_code:org_t)
2021-07-28
2021
English.
In: Nature Protocols. - : Nature Publishing Group. - 1754-2189 .- 1750-2799. ; 16:9, s. 4299-4326
Related links:
https://urn.kb.se/re...
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https://doi.org/10.1...
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  • Research review (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)

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