TissuePlot: A Multi-Scale Interactive Web App For Visualizing Spatial Data

Abstract

Visualization 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 or dot charts obscure underlying tissue regions and necessitate switching between different views for accurate interpretations. Here, we present TissuePlot, a novel method for visualizing spatial data at molecular, cellular and tissue levels in the context of their spatial locations. To this end, TissuePlot employs a transparent hexagon tessellation approach that utilizes object borders to represent cell composition or gene-level data without obscuring the underlying tissue image. Additionally, it offers a multi-view interactive web app, that allows 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.