Scellop (previously CellPop) is an interactive visualization tool for cell type compositions. Easily compare your cell type compositions with different visual encodings, filter and sort on metadata, and export for communication.
Cell type populations are commonly shown with stacked bar charts. However, scaling the number of samples and cell types in these visualizations create issues with analyzing these charts. Scellop alleviates this by presenting a flexible heatmap and side views with extending layered bar charts.
Scellop is available on NPM and PyPI.
npm i scellopIf you load data from the HuBMAP portal, also install the HuBMAP loader:
npm i @scellop/hubmap-data-loadingpip install scellopA demo is available here. How to use the ScellopComponent is shown here.
The main view and interactions:
This is a pnpm workspace monorepo.
| Path | Package | Published |
|---|---|---|
packages/scellop |
scellop |
NPM |
packages/data-loading |
@scellop/data-loading |
NPM |
packages/hubmap-data-loading |
@scellop/hubmap-data-loading |
NPM |
sites/demo |
@scellop/demo |
no (deployed to Netlify) |
python |
scellop |
PyPI |
This project uses pnpm.
Install dependencies with:
pnpm installRun the demo with:
pnpm run devOther workspace-wide commands:
pnpm build # build every package
pnpm test:run # run all tests once
pnpm typecheck # tsc --noEmit across packages
pnpm lint:fix # Biome, auto-fix
pnpm bench # performance benchmarksReleases are managed with changesets. Add one with pnpm changeset in any PR that changes a published package.
Instructions for Python package are it's own contributing guidelines.
- Thomas C. Smits (tsmits@hms.harvard.edu)
- Nikolay Akhmetov (nikolay_akhmetov@hms.harvard.edu)
- Tiffany Liaw (tiffany_liaw@hms.harvard.edu)
- Nils Gehlenborg (nils@hms.harvard.edu)
Our preprint is available here.
Please cite this work as follows:
Thomas C Smits, Nikolay Akhmetov, Tiffany S. Liaw, Mark S. Keller, Eric Moerth, Nils Gehlenborg (2025). scellop: A Scalable Redesign of Cell Population Plots for Single-Cell Data. arXiv preprint. https://10.48550/arXiv.2510.09554