A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations

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 commu...

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Bibliographic Details
Published inMetabolites Vol. 9; no. 7; p. 128
Main Authors Henglin, Mir, Niiranen, Teemu, Watrous, Jeramie D, Lagerborg, Kim A, Antonelli, Joseph, Claggett, Brian L, Demosthenes, Emmanuella J, von Jeinsen, Beatrice, Demler, Olga, Vasan, Ramachandran S, Larson, Martin G, Jain, Mohit, Cheng, Susan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 02.07.2019
MDPI
Subjects
Age
Cardiovascular disease
Chromatography
clinical outcomes research
Diabetes
epidemiology
Lipids
Metabolism
Metabolites
Metabolomics
Phenotypes
Population studies
Protocol
Rain
Trends
Visualization
visualizations
epidemiology
visualizations
clinical outcomes research
metabolomics
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Summary: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.
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These authors contributed equally to this work.
ISSN:2218-1989
2218-1989
DOI:10.3390/metabo9070128