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Proposed minimum reporting standards for data analysis in metabolomics

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Author: Goodacre, R. · Broadhurst, D. · Smilde, A.K. · Kristal, B.S. · Baker, J.D. · Beger, R. · Bessant, C. · Connor, S. · Capuani, G. · Craig, A. · Ebbels, T. · Kell, D.B. · Manetti, C. · Newton, J. · Paternostro, G. · Somorjai, R. · Sjöström, M. · Trygg, J. · Wulfert, F.
Institution: TNO Kwaliteit van Leven
Source:Metabolomics, 3, 3, 231-241
Identifier: 240163
doi: doi:10.1007/s11306-007-0081-3
Keywords: Biology · Analytical research · Bioinformatics · Biostatistics · Chemometrics · Informatics · Machine learning · Megavariate · Multivariate · Statistical learning · Statistics · Supervised learning · Unsupervised learning


The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called meta-data). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/ matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses. © Springer Science+Business Media, LLC 2007.