Visualization of GC/TOF-MS-Based Metabolomics Data for Identification of Biochemically Interesting Compounds Using OPLS Class Models

TLDR

Metabolomics generates complex data tables that are difficult to summarize, making chemometric tools such as PCA, PLS, and OPLS essential for robust modeling. The study proposes the S‑plot as a visualization tool for interpreting multivariate classification models like OPLS discriminant analysis. The S‑plot displays covariance and correlation between metabolites and class labels, and its SUS‑plot extension compares multiple models against a common reference, as applied to GC‑MS metabolomics of transgenic poplar lines versus wild type. Using OPLS, the S‑plot improved visualization and discrimination of statistically and potentially biochemically significant metabolites.

Abstract

Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools. The use of chemometrics tools, e.g., principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data. Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes. The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation. Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability. An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of multiple classification models compared to a common reference, e.g., control. The used example is a gas chromatography coupled mass spectroscopy based metabolomics study in plant biology where two different transgenic poplar lines are compared to wild type. By using OPLS, an improved visualization and discrimination of interesting metabolites could be demonstrated.