User-Guided Visual Analytics of Genome-Wide DNA Methylation Data Based on Self-Organizing Maps

DNA methylation is a key epigenetic modification with diagnostic and prognostic relevance across a wide range of diseases, particularly cancer. Modern array-based technologies enable high-throughput quantification of methylation states at hundreds of thousands of CpG sites, yielding high-dimensional datasets that pose significant challenges for exploratory analysis and feature prioritization. Existing visualization tools often lack interactivity, integration with machine learning methods, or flexible mechanisms for dynamic dimensionality reduction and biological interpretation. This work presents an interactive analytical framework that extends the Self-Organizing Map approach for epigenomic data exploration. Our method introduces metasites-representative prototypes of CpG site clusters-enabling interpretable, real-time visualization and machine learning over reduced feature spaces. Through conditional sample projections (e.g., via PCA, t-SNE, or UMAP), user-driven region selection, and the integration of sparsity-controlled logistic regression, we generate metasite relevance maps that reveal discriminative epigenetic patterns and guide downstream analysis. The proposed approach supports iterative, visually driven discovery of co-regulated modules and disease-associated methylation signatures, offering a powerful and intuitive interface for multidimensional exploration of complex methylation landscapes. Its utility is demonstrated through the analysis of DNA methylation in pheochromocytomas and paragangliomas, focusing on SDHB mutation status and the role of protocadherine gene clusters.