A Single Visualization Technique for Displaying Multiple Metabolite–Phenotype Associations

Abstract

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.

Metabolomic differences between critically Ill women and men

“…In linear regression models of metabolite data from single time points (day 0, 3 or 7), we find significant differences exist in 51 individual metabolites at 1 or more time point (all multiple test-corrected threshold of P -value < 8.65 × 10 –5 , − log 10 ( P ) > 4.06). The rain plots 32 separately show the metabolites that increase (see Fig. 1 ) or decrease (see Fig.…”

“…All linear regression models were analyzed using STATA 14.1MP 69 . Rain plots were produced based on hierarchical clustering in R-3.6.2 adapted from source code published by Henglin et al 32 .…”

Metabolomic basis for response to high dose vitamin D in critical illness

“…All mixed-effects models were analyzed using STATA 14.1 MP (College Station, TX). We employed rain plots [14] to visualize effect size, significance, clustering and trends across days 0, 3 and 7. Rain plots were produced based on hierarchical clustering in R-3.6.2.…”

One-Year Effects of Omega-3 Treatment on Fatty Acids, Oxylipins, and Related Bioactive Lipids and Their Associations with Clinical Lipid and Inflammatory Biomarkers: Findings from a Substudy of the Vitamin D and Omega-3 Trial (VITAL)

“…PA-palmitic acid, oxylipins 1,2 could not be annotated due to the lack of the standard in the library, they are novel oxylipins reported in [12]. Rainplot [17] of beta coefficients of the significant associations of one-year changes in clinical biomarkers (high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL6), tumor necrosis factor receptor 2 (TNRF2), HDLC, low-density lipoprotein cholesterol (LDLC) and triglycerides) with one-year changes in FAs, oxylipins and bioactive lipid features controlling for age, sex and race. Only significant with n-3 treatment and annotated FAs, oxylipins and bioactive lipids are reported here as well as 30 out of 143 most significant un-annotated bioactive lipid features (labeled with m/z and RT) with respect to their association with n-3 treatment.…”