RingNet: An Interactive Platform for Multi-Modal Data Visualization in Networks

Abstract

The exponential growth of data in biomedicine has created an urgent need for intuitive visualization tools. These tools should effectively represent complex biological networks and remain accessible to domain experts without extensive computational training. Current network visualization approaches often require specialized programming skills and/or cannot handle the scale and complexity of modern biomedical datasets, which creates significant barriers to biological discovery. We develop RingNet, a web-based interactive visualization tool that integrates computational efficiency with flexible, user-driven exploration. This tool addresses the communitys need to visualize multi-modal datasets within a single, compact network representation, as well as identify patterns of interest in complex data. RingNet uses an R backend for network computation and coordinate optimization. This generates JSON data structures that feed into a JavaScript and HTML frontend, which provides real-time, interactive visualization functions. It offers dynamic layout adjustments, node and edge filtering, and customizable color schemes for representing data. It can export reproducible, publication-ready figures in SVG and PNG formats. In our case studies, we use RingNet to visualize breast cancer patients omics profiles in a gene regulatory network and a cell-to-cell communication network in atopic dermatitis. This demonstrates RingNets ability to reveal biological relationships across multiple data modalities. HighlightsO_LIRingNet enables intuitive, interactive visualization of multi-modal networks without requiring advanced computational or programming expertise. C_LIO_LIRingNet has two functions: one to identify patterns in complex data at the node level, and the other to identify samples with homogeneous and heterogeneous profiles within network nodes. C_LIO_LIRingNet integrates efficient backend computation with real-time, flexible frontend exploration in a single, web-based framework. C_LIO_LIRingNet reveals cross-modal biological relationships and produces reproducible, publication-ready figures. C_LI Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/700593v4_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@7c79f2org.highwire.dtl.DTLVardef@2a0c73org.highwire.dtl.DTLVardef@97845eorg.highwire.dtl.DTLVardef@1735352_HPS_FORMAT_FIGEXP M_FIG C_FIG