Generating colorblind-friendly scatter plots for single-cell data
Abstract
Reduced-dimension or spatial in situ scatter plots are widely employed in bioinformatics papers analyzing single-cell data to present phenomena or cell-conditions of interest in cell groups. When displaying these cell groups, color is frequently the only graphical cue used to differentiate them. However, as the complexity of the information presented in these visualizations increases, the usefulness of color as the only visual cue declines, especially for the sizable readership with color-vision deficiencies (CVDs). In this paper, we present scatterHatch, an R package that creates easily interpretable scatter plots by redundant coding of cell groups using colors as well as patterns. We give examples to demonstrate how the scatterHatch plots are more accessible than simple scatter plots when simulated for various types of CVDs.
Data availability
The current manuscript is a computational study, so no new data have been generated for this manuscript. The scripts used for generating the figures in this manuscript are available at https://github.com/FertigLab/scatterHatch-paper.
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Single-cell intensity data used in Figures 7 and 8.https://doi.org/10.7554/eLife.31657.024.
Article and author information
Author details
Funding
National Cancer Institute (U01CA253403)
- Elana J Fertig
National Cancer Institute (U01CA212007)
- Elana J Fertig
National Cancer Institute (P01CA247886)
- Elana J Fertig
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Jungmin Choi, Korea University College of Medicine, Republic of Korea
Version history
- Preprint posted: October 7, 2021 (view preprint)
- Received: July 24, 2022
- Accepted: December 15, 2022
- Accepted Manuscript published: December 16, 2022 (version 1)
- Version of Record published: January 9, 2023 (version 2)
Copyright
© 2022, Guha et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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