Question about PLS-DA hyperparameter tuning [R]

A bioinformatician on Reddit surfaced a practical PLS-DA tuning workflow issue: global model diagnostics suggested 2 latent components via centroid distance, but post-sparsity performance assessment degraded after applying sPLS-DA feature selection. This highlights a known instability in mixOmics sPLS-DA where aggressive variable pruning per component can collapse discriminative variance, particularly in small-n, high-p omics datasets. Practitioners doing biomarker discovery should cross-validate component count and keepX simultaneously, not sequentially, to avoid this compounding error.