Velazquez Dee, Fan Jean
Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, United States.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States.
Bioinformatics. 2025 Feb 4;41(2). doi: 10.1093/bioinformatics/btaf047.
Displaying proportional data across many spatially resolved coordinates is a challenging but important data visualization task, particularly for spatially resolved transcriptomics data. Scatter pie plots are one type of commonly used data visualization for such data but present perceptual challenges that may lead to difficulties in interpretation. Increasing the visual saliency of such data visualizations can help viewers more accurately identify proportional trends and compare proportional differences across spatial locations.
We developed scatterbar, an open-source R package that extends ggplot2, to visualize proportional data across many spatially resolved coordinates using scatter stacked bar plots. We apply scatterbar to visualize deconvolved cell-type proportions from a spatial transcriptomics dataset of the adult mouse brain to demonstrate how scatter stacked bar plots can enhance the distinguishability of proportional distributions compared to scatter pie plots.
scatterbar is available on CRAN https://cran.r-project.org/package=scatterbar with additional documentation and tutorials at https://jef.works/scatterbar/.
在许多空间分辨坐标上展示比例数据是一项具有挑战性但又很重要的数据可视化任务，特别是对于空间分辨转录组学数据而言。散点饼图是用于此类数据的一种常用数据可视化方式，但存在感知挑战，可能导致解读困难。提高此类数据可视化的视觉显著性有助于观众更准确地识别比例趋势，并比较不同空间位置的比例差异。
我们开发了scatterbar，这是一个扩展了ggplot2的开源R包，用于使用散点堆叠条形图在许多空间分辨坐标上可视化比例数据。我们应用scatterbar来可视化来自成年小鼠脑空间转录组学数据集的解卷积细胞类型比例，以展示与散点饼图相比，散点堆叠条形图如何增强比例分布的可区分性。
scatterbar可在CRAN上获取，网址为https://cran.r-project.org/package=scatterbar，在https://jef.works/scatterbar/上有额外的文档和教程。
