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.

One of the major goals in gene expression data analysis is to explore and discover groups of genes and groups of biological conditions with meaningful relationships. While this problem can be addressed by algorithms, their results require an analysis within context, since they may be affected by many side processes -such as tissue differentiation- that could hinder the target goal. Visual analytics-based methods for exploratory analysis of the gene expression matrix (GEM) are essential in biomedical research since they allow us to frame the analysis within the user's knowledge domain. In this paper, we present a visual analytics approach to discover relevant connections between genes and samples based on linking a reordered GEM heatmap and dual 2D projections of its rows and columns, which can be recomputed conditioned by subsets of genes and/or samples selected by the user during the analysis. We demonstrate the capability of our approach to discover relevant knowledge in three case studies involving two cancer types plus normal tissue from the TCGA database.

MOTIVATION: Biomedical research entails analyzing high dimensional records of biomedical features with hundreds or thousands of samples each. This often involves using also complementary clinical metadata, as well as a broad user domain knowledge. Common data analytics software makes use of machine learning algorithms or data visualization tools. However, they are frequently one-way analyses, providing little room for the user to reconfigure the steps in light of the observed results. In other cases, reconfigurations involve large latencies, requiring a retraining of algorithms or a large pipeline of actions. The complex and multiway nature of the problem, nonetheless, suggests that user interaction feedback is a key element to boost the cognitive process of analysis, and must be both broad and fluid. RESULTS: In this article, we present a technique for biomedical data analytics, based on blending meaningful views in an efficient manner, allowing to provide a natural smooth way to transition among different but complementary representations of data and knowledge. Our hypothesis is that the confluence of diverse complementary information from different domains on a highly interactive interface allows the user to discover relevant relationships or generate new hypotheses to be investigated by other means. We illustrate the potential of this approach with three case studies involving gene expression data and clinical metadata, as representative examples of high dimensional, multidomain, biomedical data. AVAILABILITY AND IMPLEMENTATION: Code and demo app to reproduce the results available at https://gitlab.com/idiazblanco/morphing-projections-demo-and-dataset-preparation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.