Visualizing metabolomics data with R

In communicating scientific results, convincing data visualization is of utmost importance. Especially in metabolomics, results based on large numbers of dimensions and variables necessitate particular attention in order to convey their message unambiguously to the reader; also, in the era of open science, traceability and reproducibility are becoming increasingly important. This article describes the use of the R programming language to visualize published metabolomics data resulting from ex vivo NMR spectroscopy and mass spectrometry experiments with a special focus on reproducibility, including example figures as well as associated R code for ease of reuse. Examples include various types of plots (bar plots, swarm plots, and violin plots; volcano plots, heatmaps, Euler diagrams, Kaplan-Meier survival plots) and annotations (groupings, intragroup line connections, significance brackets, text annotations). Advantages of code-generated plots as well as advanced techniques and best practices are discussed.