Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs

Making sense of association networks 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 traditional visualizations such as heatmaps quickly become too complicated to parse effectively. Here we present associationSubgraphs: a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network science-derived statistics and visualization. As a use case example, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record (EHR) and provided an online, interactive demonstration for exploring multimorbidity subgraphs.