The general nuclear magnetic resonance analysis toolbox (GNAT) has established itself as a versatile free and open-source software suite for processing and analyzing NMR data. Here, we present a major expansion for comprehensive metabolomics analysis. The module implements a whole NMR metabolomics pipeline in a single piece of software. This expansion includes a new suite of powerful statistical tools designed for tasks like classification, discrimination, and correlation analysis, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal projections to latent structures-discriminant analysis (OPLS-DA), and statistical total correlation analysis (STOCSY). A set of preprocessing tools for binning and variable selection (interval partial least squares regression, iPLS and backward interval partial least squares regression, biPLS) before metabolomics analysis is also available. Graphical tools for outlier detection allow researchers to identify and address potential data inconsistencies. Model validation and application to unknown samples are straightforward and supported by relevant analytical figures of merit. All analyses done in the toolbox can be exported as reports in various formats (including .txt, .xml, and .mat). The standard version of the GNAT is intended to be run within MATLAB, but standalone compiled versions are also available. The new functionalities are demonstrated using a test data set for the classification of edible oils.
Rocha et al. (Mon,) studied this question.