MOTIVATION: Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. RESULTS: Here, we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. AVAILABILITY AND IMPLEMENTATION: An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https://github.com/tbilab/associationsubgraphs. Online documentation is available at https://prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https://prod.tbilab.org/associationsubgraphs-example/.

BACKGROUND: Precision oncology increasingly utilizes molecular profiling of tumors to determine treatment decisions with targeted therapeutics. The molecular profiling data is valuable in the treatment of individual patients as well as for multiple secondary uses. OBJECTIVE: To automatically parse, categorize, and aggregate clinical molecular profile data generated during cancer care as well as use this data to address multiple secondary use cases. METHODS: A system to parse, categorize and aggregate molecular profile data was created. A naÿve Bayesian classifier categorized results according to clinical groups. The accuracy of these systems were validated against a published expertly-curated subset of molecular profiling data. RESULTS: Following one year of operation, 819 samples have been accurately parsed and categorized to generate a data repository of 10,620 genetic variants. The database has been used for operational, clinical trial, and discovery science research. CONCLUSIONS: A real-time database of molecular profiling data is a pragmatic solution to several knowledge management problems in the practice and science of precision oncology.