Next Article in Journal
Metabolic Perturbations from Step Reduction in Older Persons at Risk for Sarcopenia: Plasma Biomarkers of Abrupt Changes in Physical Activity
Next Article in Special Issue
MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools
Previous Article in Journal
Effects of Gut Microbiota on the Bioavailability of Bioactive Compounds from Ginkgo Leaf Extracts
Previous Article in Special Issue
Mass Spectrometry Data Repository Enhances Novel Metabolite Discoveries with Advances in Computational Metabolomics
Open AccessArticle

Visualization and Interpretation of Multivariate Associations with Disease Risk Markers and Disease Risk—The Triplot

1
Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden
2
Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
3
Department of Epidemiology and Biostatistics, School of Public Health, Xi’an Jiaotong University Health Science Center, SE-710049 Xi’an, China
4
Department of Public Health and Clinical Medicine, Umeå University, SE-901 87 Umeå, Sweden
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Metabolites 2019, 9(7), 133; https://doi.org/10.3390/metabo9070133
Received: 18 June 2019 / Revised: 1 July 2019 / Accepted: 3 July 2019 / Published: 6 July 2019
Metabolomics has emerged as a promising technique to understand relationships between environmental factors and health status. Through comprehensive profiling of small molecules in biological samples, metabolomics generates high-dimensional data objectively, reflecting exposures, endogenous responses, and health effects, thereby providing further insights into exposure-disease associations. However, the multivariate nature of metabolomics data contributes to high complexity in analysis and interpretation. Efficient visualization techniques of multivariate data that allow direct interpretation of combined exposures, metabolome, and disease risk, are currently lacking. We have therefore developed the ‘triplot’ tool, a novel algorithm that simultaneously integrates and displays metabolites through latent variable modeling (e.g., principal component analysis, partial least squares regression, or factor analysis), their correlations with exposures, and their associations with disease risk estimates or intermediate risk factors. This paper illustrates the framework of the ‘triplot’ using two synthetic datasets that explore associations between dietary intake, plasma metabolome, and incident type 2 diabetes or BMI, an intermediate risk factor for lifestyle-related diseases. Our results demonstrate advantages of triplot over conventional visualization methods in facilitating interpretation in multivariate risk modeling with high-dimensional data. Algorithms, synthetic data, and tutorials are open source and available in the R package ‘triplot’. View Full-Text
Keywords: triplot; metabolomics; multivariate risk modeling; environmental factors; disease risk triplot; metabolomics; multivariate risk modeling; environmental factors; disease risk
Show Figures

Figure 1

MDPI and ACS Style

Schillemans, T.; Shi, L.; Liu, X.; Åkesson, A.; Landberg, R.; Brunius, C. Visualization and Interpretation of Multivariate Associations with Disease Risk Markers and Disease Risk—The Triplot. Metabolites 2019, 9, 133.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop