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scellop: A Scalable Redesign of Cell Population Plots for Single-Cell Data
October 10, 2025 Β· Declared Dead Β· π arXiv.org
Authors
Thomas C. Smits, Nikolay Akhmetov, Tiffany S. Liaw, Mark S. Keller, Eric MΓΆrth, Nils Gehlenborg
arXiv ID
2510.09554
Category
cs.HC: Human-Computer Interaction
Cross-listed
q-bio.QM
Citations
1
Venue
arXiv.org
Repository
https://github.com/hms-dbmi/scellop
Last Checked
2 months ago
Abstract
Summary: Cell population plots are visualizations showing cell population distributions in biological samples with single-cell data, traditionally shown with stacked bar charts. Here, we address issues with this approach, particularly its limited scalability with increasing number of cell types and samples, and present scellop, a novel interactive cell population viewer combining visual encodings optimized for common user tasks in studying populations of cells across samples or conditions. Availability and Implementation: Scellop is available under the MIT licence at https://github.com/hms-dbmi/scellop, and is available on PyPI (https://pypi.org/project/cellpop/) and NPM (https://www.npmjs.com/package/cellpop). A demo is available at https://scellop.netlify.app/.
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