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- Antti, Henrik, et al.
(författare)
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Batch statistical processing of 1H NMR-derived urinary spectral data
- 2002
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Ingår i: Journal of Chemometrics: Special Issue: Proceedings of the 7th Scandinavian Symposium on Chemometrics. Issue Edited by Lars Nørgaard. - : Wiley. ; 16:8-10, s. 461-8
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate statistical batch processing (BP) analysis of 1H nuclear magnetic resonance (NMR) urine spectra was employed to establish time-dependent metabolic variations in animals treated with the model hepatotoxin hydrazine. Hydrazine was administered orally to rats (at 90 mg kg-1), and urine samples were collected from dosed rats and matched control animals (n = 5 per group) at time points up to 168 h post-dose. Urine samples were analysed via 1H NMR spectroscopy and partial least squares-based batch processing analysis, treating each rat as an individual batch comprising a series of timed urine samples. A model defining the mean urine profile was established for the control group, and samples obtained from hydrazine-treated animals were assessed using this model. Time-dependent deviations from the control model were evident in all hydrazine-treated animals, and hepatotoxicity was manifested by increased urinary excretion of taurine, creatine, 2-aminoadipate, citrulline and -alanine together with depletion of urinary levels of citrate, succinate and hippurate. The experiment was repeated at six different pharmaceutical centres in order to assess the robustness of the technology and to establish the natural variability in the data. Results were consistent across the data for all centres. BP plots showed a characteristic pattern for each toxin, allowing the time points at which there were maximum metabolic differences to be determined and providing a means of visualizing the net toxin-induced metabolic movement of urinary metabolism. BP may prove to be a powerful metabonomic tool in defining time-dependent metabolic consequences of toxicity and is an efficient means of visualizing inter-animal variations in response as well as defining multivariate statistical limits of normality in terms of biofluid composition.
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| 2. |
- Blaise, Benjamin J., et al.
(författare)
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Statistical analysis in metabolic phenotyping
- 2021
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Ingår i: Nature Protocols. - : Nature Publishing Group. - 1754-2189 .- 1750-2799. ; 16:9, s. 4299-4326
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Forskningsöversikt (refereegranskat)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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