Fig. 7
(A) PLS-DA scatter plot in SIMCA representing sample distribution based on metabolomics profiles. (B) PLS-DA loading plot showing metabolite contributions to group separation. Axes wc1 and wc2 account for 28.6% and 21.7% variance, respectively. Alpha-glucose, methyl histidine, and choline strongly influence separation, while clustered metabolites (valine, leucine, isoleucine) suggest common pathways. Metabolites near the origin (citrate, methionine) contribute less variance. (C) Permutation plot validating PLS-DA model robustness with R2 (green circles) and Q2 (blue squares) over 20 permutations and 7 components. Higher R2 and Q2 values indicate better model performance and minimal overfitting.
