The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.
🚨new preprint alert 🚨— Boyi Guo 郭博逸 (@BoyiGuo) March 28, 2023
⭐️escheR: Unified multi-dimensional visualizations with Gestalt principles⭐️
We introduce Gestalt principles to optimize spatial visualizations, specifically joint visualization of multi-dimensional data. (🧵) pic.twitter.com/gYBbMbEBW6