Problem
Research questions and friction points this paper is trying to address.
spatial transcriptomics
visualization
high-dimensional
multimodal
uncertain
How Do We Visualize Space in Molecular Biology? A Study of Spatial Transcriptomics Visualization Practices
🤖 AI Summary
本文研究了空间转录组学可视化实践，通过调查148篇论文和1824个图表面板，评估现有方法并指出未来挑战。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Spatial Transcriptomics
Visualization Practices
What-Why-How Coding Framework
Interactive Visualization Software
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