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A Single Visualization Technique for Displaying Multiple Metabolite–Phenotype Associations

1
Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
2
National Institute for Health and Welfare, FI 00271 Helsinki, Finland
3
Department of Medicine, Turku University Hospital and University of Turku, FI 20521 Turku, Finland
4
Departments of Medicine & Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
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Department of Statistics, University of Florida, Gainesville, FL 32611, USA
6
Framingham Heart Study, Framingham, MA 01701, USA
7
Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
8
Preventive Medicine, Department of Medicine, Boston University Medical Center, Boston, MA 02215, USA
9
Biostatistics Department, School of Public Health, Boston University, Boston, MA 02215, USA
10
Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Metabolites 2019, 9(7), 128; https://doi.org/10.3390/metabo9070128
Received: 30 April 2019 / Revised: 28 June 2019 / Accepted: 28 June 2019 / Published: 2 July 2019
(This article belongs to the Special Issue Metabolomics in Epidemiological Studies)
To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel ‘rain plot’ approach to display the results of these analyses. The ‘rain plot’ combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically. View Full-Text
Keywords: metabolomics; visualizations; clinical outcomes research; epidemiology metabolomics; visualizations; clinical outcomes research; epidemiology
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Henglin, M.; Niiranen, T.; Watrous, J.D.; Lagerborg, K.A.; Antonelli, J.; Claggett, B.L.; Demosthenes, E.J.; von Jeinsen, B.; Demler, O.; Vasan, R.S.; Larson, M.G.; Jain, M.; Cheng, S. A Single Visualization Technique for Displaying Multiple Metabolite–Phenotype Associations. Metabolites 2019, 9, 128.

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