How spatial transcriptomics visualizations map biological space

How Do We Visualize Space in Molecular Biology? A Study of Spatial Transcriptomics Visualization Practices

Human-Computer Interaction

Summary

Understanding where cells are located in tissue helps scientists learn how they behave differently based on their positions, like immune cells near or inside tumors. Spatial transcriptomics is a new way to capture both the gene activity in cells and their exact locations, but this creates complex data that are hard to show clearly in pictures. The authors studied many scientific papers and their images to see how researchers currently visualize this data and which approaches work best. They also looked at interactive tools that help explore this type of data. Their study highlights what visualization methods are common now and what challenges remain for making better visual tools in this area.

What this means in practice

A survey. It maps existing work.

Authors

Denisse Chacón-Ramírez, Mark S. Keller, Eric Mörth, Nils Gehlenborg, Marc Streit, Andreas Hinterreiter

Abstract

A cell's identity depends on where it sits in tissue: for example, a macrophage behaves differently in a tumor core than at its edge. Spatial transcriptomics has transformed how we study this by recovering that lost coordinate, but it does so by producing data that is simultaneously high-dimensional, multimodal, and uncertain. Visualizing this combination is a hard problem in its own right, and one that warrants an assessment of how the field currently represents it, what has worked, and what is still missing. We surveyed 148 papers and 1,824 figure panels using a What-Why-How coding framework grounded in Munzner's nested model, connecting the data represented, the biological tasks motivating each visualization, and the design choices through which they are expressed; a subset of the surveyed work also contributed dedicated interactive visualization software that was not necessarily reflected in the static figures, and we looked at what interaction capabilities those tools supported as well. We close by outlining where the field stands and the challenges ahead for bioinformatics and visualization researchers to tackle together.
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