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* refactor reducer to no longer support hover state and hold many labels * refactor to generate centroidCoordinates for all values of a category * create hash for function and memoize export * create button to display all labels for a category * clear state * create label for each thing * calculate on each value * change to in place modification of map * switch to for loop with iterator instead of forEach * use map from centroidLabel instead of creating copy * adapt for map * utilize tarrays * begin documentation * disable centroids if in zoom mode * clean up * persist uncalc coordinates * document * clean up and document * cleanup and document * fix * fix undefined labels and document changes * fix first element skip * fix conditional recalc * rename centroidLabel -> centroidLabels * break out dilation on hover to new reducer * numerous styling changes for readability * change centroid icon * remove colorAccessor from parameters * recalc centroids on world change * make label toggle undoable * remove unused import * highlight labels on hover * remove special characters from svg id * lighten backdrop * only generate new centroids if they pre-exist * fix issue with spaces in catagorical value name * add label buttons to menubar * change reducer to use colorAccessor and have single toggle * fix check to see if svg should be rendered * move svg overlays onto a single svg layer * dilate on label hover * remove logs * allow centroid to update along side regl renders * allow actions to pass through svg if in zoom mode * remove artifact from circle * remove comment * remove disabling of centroid button * fix conditional map to screen * make styling label conditions stricter * prettier * refactor onto master * refactor computePointFlags() to use pointDilation store * notify when viewport changes * move svg attributes out of lasso setup and prevent rerenders/writes * begin playing with transform matrix * first solution for camera interaction * create transform using nested groups * semi-working method using nested groups with transforms * inversely scale text * properly do final transform * cleanup dead / test code * reinstate original functionality * breakout centroid labels labels into separate component * default toggle on for testing * separate lasso and centroid layers * remove unnecessary attributes, working hover * dilation on label hover * fix dilation on scatterplot * add dilation on label hover * break overlay into separate component * make overlay agnostic to children * move label mouse actions to centroidlabels component, add overlay state * remove lasso on switch to camera * disallow user selection * fix reducer * fix subset with continuous color error * reset labels on color by continuous * revert centroids on by default * refactor for nested restructuring * remove update checking * remove unused method * readd deleted hover delay * remove old centroid setup * remove centroid from undoable * cleanup dead code * remove dead code * rollback unnecessary changes * begin adding annotation functionality * add annotation functionality * add reset and undo functionality * change centroids on layout change * don't create label for unassigned * add comment pointing out POI for performance * touch up matrix transform comment * add comment explaining coordinate space and children's assumed space * remove dead code * switch to pure component * connect centroidLabels to redux * clean up camera check and null result * tool tip change * rename centroid toggle and the like * fix the misalignment of buttons, also make blueprint use consistent * fix comment spelling mistakes * introduce variable for cleaner logic expressions and state assignment * alter tooltip text to back color by interaction * remove manual iterator manipulation for forEach() * remove debounce * nit fix * tooltip wording fix * lint
an interactive explorer for single-cell transcriptomics data
cellxgene (pronounced "cell-by-gene") is an interactive data explorer for single-cell transcriptomics datasets, such as those coming from the Human Cell Atlas. Leveraging modern web development techniques to enable fast visualizations of at least 1 million cells, we hope to enable biologists and computational researchers to explore their data.
Whether you need to visualize one thousand cells or one million, cellxgene helps you gain insight into your single-cell data.
Getting started
The comprehensive guide to cellxgene
The cellxgene documentation is your one-stop-shop for information about cellxgene! You may be particularly interested in:
- Seeing what cellxgene can do
- Learning more about cellxgene installation and usage
- Preparing your own data for use in cellxgene
- Checking out our roadmap for future development
- Contributing to cellxgene
Quick start
To install cellxgene you need Python 3.6+. We recommend installing cellxgene into a conda or virtual environment.
Install the package.
pip install cellxgene
Launch cellxgene with an example anndata file
cellxgene launch https://cellxgene-example-data.czi.technology/pbmc3k.h5ad
To explore more datasets already formatted for cellxgene, check out the Demo data or see Preparing your data to learn more about formatting your own data for cellxgene.
Finding help
We'd love to hear from you!
For questions, suggestions, or accolades, join the #cellxgene-users channel on the CZI Science Slack and say "hi!".
For any errors, report bugs on Github.
Developing with cellxgene
Contributing
We warmly welcome contributions from the community! Please see our contributing guide and don't hesitate to open an issue or send a pull request to improve cellxgene.
This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to opensource@chanzuckerberg.com.
Reuse
This project was started with the sole goal of empowering the scientific community to explore and understand their data. As such, we encourage other scientific tool builders in academia or industry to adopt the patterns, tools, and code from this project, and reach out to us with ideas or questions. All code is freely available for reuse under the MIT license.
Security
If you believe you have found a security issue, we would appreciate notification. Please send email to security@chanzuckerberg.com.
About
Core team
The current core team:
- Colin Megill, frontend & product design
- Bruce Martin, software engineer
- Sidney Bell, computational biologist
- Lia Prins, designer
- Severiano Badajoz, software engineer
We would also like to gratefully acknowledge contributions from past core team members:
- Charlotte Weaver, software engineer
Inspiration
We've been heavily inspired by several other related single-cell visualization projects, including the UCSC Cell Browswer, Cytoscape, Xena, ASAP, Gene Pattern, and many others. We hope to explore collaborations where useful as this community works together on improving interactive visualization for single-cell data.
We were inspired by Mike Bostock and the crossfilter team for the design of our filtering implementation.
We have been working closely with the scanpy team to integrate with their awesome analysis tools. Special thanks to Alex Wolf, Fabian Theis, and the rest of the team for their help during development and for providing an example dataset.
We are eager to explore integrations with other computational backends such as Seurat or Bioconductor