DOI: 10.18129/B9.bioc.escheR  

Unified multi-dimensional visualizations with Gestalt principles

Bioconductor version: Release (3.17)

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 this 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.

Author: Boyi Guo [aut, cre] , Stephanie C. Hicks [aut]

Maintainer: Boyi Guo < at>

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biocViews SingleCell, Software, Spatial, Transcriptomics, Visualization
Version 1.0.0
In Bioconductor since BioC 3.17 (R-4.3) (< 6 months)
License MIT + file LICENSE
Depends ggplot2, R (>= 4.3)
Imports SpatialExperiment(>= 1.6.1), spatialLIBD(>= 1.11.3), rlang, SummarizedExperiment
Suggests STexampleData, knitr, rmarkdown, BiocStyle
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