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Statistical Workflow for Feature Selection in Human Metabolomics Data

1
Department of Statistics, University of Florida, Gainesville, FL 32611, USA
2
Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
3
Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
4
Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
5
Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
6
National Institute for Health and Welfare, FI 00271 Helsinki, Finland
7
Department of Medicine, Turku University Hospital and Univesity of Turku, FI 20521 Turrku, Finland
8
Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
9
Framingham Heart Study, Framingham, MA 01701, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Metabolites 2019, 9(7), 143; https://doi.org/10.3390/metabo9070143
Received: 30 April 2019 / Revised: 3 July 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease. Large-scale metabolomics data sources, generated using either targeted or nontargeted platforms, are becoming more common. Appropriate statistical analysis of these complex high-dimensional data will be critical for extracting meaningful results from such large-scale human metabolomics studies. Therefore, we consider the statistical analytical approaches that have been employed in prior human metabolomics studies. Based on the lessons learned and collective experience to date in the field, we offer a step-by-step framework for pursuing statistical analyses of cohort-based human metabolomics data, with a focus on feature selection. We discuss the range of options and approaches that may be employed at each stage of data management, analysis, and interpretation and offer guidance on the analytical decisions that need to be considered over the course of implementing a data analysis workflow. Certain pervasive analytical challenges facing the field warrant ongoing focused research. Addressing these challenges, particularly those related to analyzing human metabolomics data, will allow for more standardization of as well as advances in how research in the field is practiced. In turn, such major analytical advances will lead to substantial improvements in the overall contributions of human metabolomics investigations. View Full-Text
Keywords: statistical methods; large-scale metabolomics; high-dimensional data statistical methods; large-scale metabolomics; high-dimensional data
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Antonelli, J.; Claggett, B.L.; Henglin, M.; Kim, A.; Ovsak, G.; Kim, N.; Deng, K.; Rao, K.; Tyagi, O.; Watrous, J.D.; Lagerborg, K.A.; Hushcha, P.V.; Demler, O.V.; Mora, S.; Niiranen, T.J.; Pereira, A.C.; Jain, M.; Cheng, S. Statistical Workflow for Feature Selection in Human Metabolomics Data. Metabolites 2019, 9, 143.

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