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

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.

An optimized procedure for metabonomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry

“…The percentages of variance explained by the first and second components in the model are 23.6% and 16.0%, respectively. Variable importance in the projection (VIP) ranks the overall contribution of each variable (peak intensity) to the OPLS-DA model-variables with VIP > 1 were selected as differential signals [9] and [12]. Accordingly, we identified a total of 18 differential metabolites in our study using NIST library, and 11 of them were confirmed by standard compounds (Table 6).…”

“…Metabonomic studies generate complex multivariate data that requires chemometrics for interpretation [6]. With unsupervised methods like principle components analysis (PCA) [7], and supervised methods such as partial least square (PLS) [8] and orthogonal partial least square (OPLS) analysis [9], researchers can obtain classification patterns and identify the compounds responsible for such patterns and grouping.…”

Metabolic profiling reveals disorder of amino acid metabolism in four brain regions from a rat model of chronic unpredictable mild stress

“…Great efforts have been made to test the reliability of multivariate models [38,39], hence the 6-round cross-validation in SMICA-P software was herein applied to validate the OPLS-DA model against over-fitting by precluding 1/6th of all the samples in each round. The crossvalidated OPLS-DA scores map depicts the between-class separation (t p ) and predictive ability (Q 2 Y) simultaneously [32]. Each individual in this map is represented by two spatial dots: one for model score value (t p ) and the other for cross-validated score value (t cv ).…”

“…Variable selection is an important step in multivariate analysis that can apparently enhance our understanding and interpretability of multivariate models and commonly referred to VIP statistics, loading weights, and correlation coefficients [28][29][30]. However, the practical use of these methods relies mostly on the experimental designs and purposes (e.g., animal or human studies, biomarker identification or pathway analysis), the size of samples, and preference of researchers as well [31,32]. A strict approach for selecting significant and reliable variables should likely be a combination of multiple criteria.…”

Identification of Urinary Biomarkers Related to Cisplatin-Induced Acute Renal Toxicity Using NMR-Based Metabolomics

“…CART or logistic regression may not perform best in the presence of large intra-group variation. In comparison, Orthogonal Projections to Latent StructuresDiscriminant Analysis (OPLS-DA), is a supervised discrimination method that can deal with large intra-group variation, and thus provide markers that can be interpreted straightforwardly (Bylesjo et al, 2006;Wiklund et al, 2008). In addition, the approach gives a prediction model that can be used to test unknown samples for the presence of the property of interest.…”