Fig. 2

Assessing exploratory PLS-DA model performance and evaluating latent component and features to retain for sparse PLS-DA modeling. (a, c, e) Assessing PLS-DA model performance in the 4-class setting (Control vs. DDE vs. X-ALD vs. ZS) and the 2-class setting (X-ALD vs. ZDC; ZS vs. XDC) and selection of distance metric and number of latent components. Repeated stratified cross-validation (100  ×  5–fold CV) is used to evaluate the PLS-DA classification performance (measured by balanced error rate) for each prediction distance (max.dist, centroids.dist, and mahalanobis.dist). The balanced error rate appears to decrease negligibly after four latent components in the 4-class setting, and the balanced error rate reaches minimal value in 2-class setting with 1 latent component. (b) Cross-validation and error evaluation of the PLS-DA model in 4-class setting with 4 latent components and all 16 features. Optimal, error minimizing set of features per component are indicated with a diamond. Yellow diamond points to a 3-latent component model with 1, 15, and 1 retained feature(s) per latent components LC1, LC2, and LC3 respectively. (d) Cross-validation and error evaluation of the PLS-DA model in X-ALD vs. ZDC 2-class setting with 1 latent component and all 16 features. blue diamond points to a 1 latent component model with 8 retained features. (f) Cross-validation and error evaluation of the PLS-DA model in ZS vs. XDC 2-class setting with 1 latent component and all 16 features. Blue diamond points to a 1 latent component model with 15 retained features