Coupling bootstrap with synergy self-organizing map-based orthogonal partial least squares discriminant analysis:...

Biomarker selection has played an increasingly important part in modern medicine with advances of omics techniques. Kohonen self-organizing map is a well-established variable reduction algorithm in identifying significant biomarkers based on variable clustering. However, high dimensionality but small sample size of omics data makes self-organizing map-based model problematic in terms of selection stability and reproducibility. A novel feature screening system is presented in this study by coupling bootstrap with synergy self-organizing map-based orthogonal partial least squares discriminant analysis for stable and biologically meaningful metabolic biomarker selection. In the proposed feature screening system, particle swarm optimization algorithm is utilized to configure synergy self-organizing map-based orthogonal partial least squares discriminant analysis to perform the combination of clusters in a heuristic learning manner, enabling flexible selection of more informative features cost-effectively. Based on the paradigm of ensemble feature selection, bootstrap is adopted to explore significant variables consistently identified across multiple feature selectors rather than a single one. The feasibility of the novel feature screening system is evaluated by two most common inherited metabolic diseases, methylmalonic academia and propionic academia, using urinary metabolomics data. With the desirable classification performance, the proposed feature screening system outperforms simpler techniques in the identification of more features closely correlated with the metabolic mechanisms and the stability of selected candidate biomarkers against sample variations. Besides, the novel feature screening system greatly degrades the sensitivity of identified candidate biomarkers to the network size of self-organizing map, benefiting the identification of a suitable and stable final candidate biomarker list.