TissuePlot: A Multi-Scale Interactive Visualization Tool for Spatial Data

AO_SCPLOWBSTRACTC_SCPLOWVisualization of spatial datasets is essential for understanding biological systems that are composed of several interacting cell types. For example, gene expression data at the molecular level needs to be interpreted based on cell type, spatial context, tissue type, and interactions with the surrounding environment. Recent advances in spatial profiling technologies allow measurements of the level of thousands of proteins or genes at different spatial locations along with corresponding cellular composition. Representing such high dimensional data effectively to facilitate data interpretation is a major challenge. Existing methods such as spatially plotted pie charts obscure underlying tissue regions and necessitate switching between different views for comprehensive interpretations. Here, we present TissuePlot, a novel method for visualizing spatial data. TissuePlot tackles the key challenge of visualizing multi-scale phenotypic data at molecular, cellular and tissue level in the context of their spatial locations. To this end, TissuePlot employs a transparent hexagon tesselation approach that utilizes object borders to represent cell composition or gene-level data without obscuring the underlying cell image. Moreover, we implement a multi-view interactive approach, to allow interrogating spatial tissue data at multiple scales linking molecular information to tissue anatomy and motifs. We demonstrate TissuePlot utility using mouse brain data from the Bio+MedVis Redesign Challenge 2024. Our tool is accessible at https://sailem-group.github.io/TissuePlot/.