Tissue-Cut Visualization (BioVis 2024 Challenge)

Spatial transcriptomics datasets contain large volumes of multidimensional biological information that combine gene expression values with spatial tissue coordinates. Traditional data visualization tools struggle to effectively render and explore such datasets interactively. Researchers often face challenges including: • Difficulty linking gene expression data with spatial tissue locations • Lack of interactive tools for exploring multimodal biological datasets • Performance limitations when visualizing high-resolution spatial data These limitations hinder effective biological interpretation of spatial omics datasets.

A browser-based visualization platform was developed to enable interactive exploration of spatial gene expression data. The system integrates high-resolution tissue imagery with gene expression information and allows users to dynamically select genes and visualize their spatial distribution across tissue samples. The application supports real-time visualization updates, enabling researchers to explore gene patterns without reloading datasets.