Abstract
NMR data from large studies combining multiple cohorts is becoming common in large-scale metabolomics. The data size and combination of cohorts with diverse properties leads to special problems for data processing and analysis. These include alignment, normalization, detection and removal of outliers, presence of strong correlations, and the identification of unknowns. Nonetheless, these challenges can be addressed with suitable algorithms and techniques, leading to enhanced data sets ripe for further data mining.
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Acknowledgments
I.K. and T.E. acknowledge support from the EU PhenoMeNal project (Horizon 2020, 654241). I.K. acknowledges support from the UK Dementia Research Institute, which is supported by the MRC, the Alzheimer’s Society and Alzheimer’s Research UK. T.E. and G.G. acknowledge support by National Institutes of Health (R01HL133932).
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Ebbels, T.M.D., Karaman, I., Graça, G. (2019). Processing and Analysis of Untargeted Multicohort NMR Data. In: Gowda, G., Raftery, D. (eds) NMR-Based Metabolomics. Methods in Molecular Biology, vol 2037. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9690-2_25
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DOI: https://doi.org/10.1007/978-1-4939-9690-2_25
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