发表机构
Johannes Kepler University Linz; Harvard Medical School（林茨约翰内斯·开普勒大学; 哈佛医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过调查148篇论文和1,824个图形面板，基于Munzner嵌套模型分析空间转录组学可视化实践，揭示数据表示、生物学任务与设计选择间的联系，并指出该领域面临的挑战。
AI 中文摘要
细胞的特性取决于其在组织中的位置：例如，巨噬细胞在肿瘤核心与肿瘤边缘的行为截然不同。空间转录组学通过恢复丢失的坐标信息，彻底改变了我们研究这一现象的方式，但代价是产生同时具有高维性、多模态性和不确定性的数据。可视化这一组合本身就是一个难题，值得评估该领域目前如何表征它、哪些方法行之有效，以及哪些方面仍然缺失。我们采用基于Munzner嵌套模型的What-Why-How编码框架，调查了148篇论文和1,824个图形面板，将所表示的数据、驱动每种可视化的生物学任务以及表达这些任务的设计选择联系起来；部分被调查的工作还贡献了专门的交互式可视化软件，这些软件未必体现在静态图形中，我们也考察了这些工具所支持的交互功能。最后，我们概述了该领域的现状以及生物信息学和可视化研究者未来需共同应对的挑战。
英文摘要
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.
CommentsSubmitted to IEEE Transactions on Visualization and Computer Graphics
