Enhancement of Plant Metabolite Fingerprinting by Machine Learning  

Abstract

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by 1 H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, 1 H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true-and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Opposite variations in fumarate and malate dominate metabolic phenotypes of Arabidopsis salicylate mutants with abnormal biomass under chilling

“…Of technical interest for breeding crops with increased biomass (Meyer et al , Steinfath et al , Sulpice et al ) was our use of NMR fingerprints to model shoot biomass. This supports the credibility of spectral fingerprinting (Scott et al , ) as an alternative to more time‐consuming chromatography (Lisec et al , Sulpice et al ).…”

Multiclass methods in the analysis of metabolomic datasets: The example of raspberry cultivar volatile compounds detected by GC–MS and PTR-MS

“…Addressing multiclass separation in metabolomics requires more sophisticated tools, such as machine learning methods (Boccard et al, 2010;Pers, Albrechtsen, Holst, Sorensen, & Gerds, 2009;Scott et al, 2010). Here we discuss the use of supervised classification methods to actually assess the separability of classes.…”

Plant Metabolomics

“…Therefore, this method belongs to a completely different system from those on the basis of PCA pattern recognition. For less-than-ideal analysis results that processed by conventional methods, RF can always get satisfactory results (Diaz-Uriarte and de Andres 2006; Amaratunga et al 2008;Statnikov et al 2008;Jiang et al 2009;Scott et al 2010). …”