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. 2012 Aug 30;7(8):e44179. doi: 10.1371/journal.pone.0044179

Figure 2. Metabolite profiling distinguishes between HBW subclasses with >62% accuracy.

Figure 2

Multivariate discriminant analysis of the high-quality ion list, consisting of 935 ions in 27 wheat lines, was used to distinguish between subclasses of hard bread wheat (HBW) comprising hard white winter (HWW), hard white spring (HWS), hard red winter (HRW), and hard red spring (HRS). (Panel 2A) To visualize inherent clustering patterns, the scatter plot depicts unsupervised analysis through the PCA model. Model fit: R2X(cum) = 40.3%, with 3 components, and Q2(cum) = 10.8%. (Panel 2B) To determine contributing sources of variation, the scatter plot represents supervised analysis of the OPLS-DA model. Near-complete separation of subclasses was observed. Model fit: R2Y(cum) = 36.6%, Q2Y(cum) = 17.3%. (Panel 2C-Inset) The misclassification table for the OPLS-DA model indicates that approximately 63% of wheat lines (17 out of 27 lines) were correctly classified, with low probability (p = 1.40E−05) of random table generation as assessed by Fisher’s Exact Probability. (Panel 2C) To visualize the misclassification rate, the dendrogram was constructed using single linkage hierarchical clustering and sorted by size. Two main clusters comprise 1) HRS and 2) the other 3 subclasses, which do not cluster by subclass, indicating a high degree of chemical homogeneity and therefore resistance to clustering by hierarchical methods between HBW subclasses.