c0263f6ce5
Github Actions Workflow updates compatibility matrix: - Added MacOS Catalina and Big Sur to test compat matrix - Added Python 3.9 to test compat matrix, but avoid running 3.9 for matrix jobs that do not have `tables` pypi build available for the given env - Maintains running tests on both cellxgene main branch and latest pypi release. - Add explicit matrix exclusions for matrix combinations that will never pass (see comments). - Numerous refactorings to the workflow config to simplify matrix. Basically a rewrite. - The anndata pkg is now tested at a pinned release and at latest release, but no longer using `master` branch version. To limit cross-product explosion of matrix jobs, the pinned anndata version is only tested on py3.8 and cellxgene latest release. - Run unit and smoke tests in a single job, to improve speed, reduce workflow complexity and the number of jobs. Also fixes the redundant testing of unit tests. Within each job, the unit and smoke tests are run in separate steps for ease of troubleshooting. - Fixed termination of backend server to allow both smoke tests to run within a single job (both attempt to use 5005 port, sequentially, but first server was not being terminated). - Replaced `continue-on-error: true` with `fail-fast: false`, which allows all matrix jobs to run independently, while also ensuring the that entire workflow is flagged as failed if any matrix job fails - The `smoke-test-annotations` fail intermittently and have been disabled. Fix will be addressed in story: https://app.zenhub.com/workspaces/single-cell-5e2a191dad828d52cc78b028/issues/chanzuckerberg/cellxgene/2433
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.
Supported browsers
cellxgene currently supports the following browsers:
- Google Chrome 61+
- Edge 15+
- Firefox 60+
Please file an issue if you would like us to add support for an unsupported browser.
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. Please see the dev_docs for pull request suggestions, unit test details, local documentation preview, and other development specifics.
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. All code is freely available for reuse under the MIT license.
Before extending cellxgene, we encourage you to reach out to us with ideas or questions. It might be possible that an extension could be directly contributed, which would make it available for a wider audience, or that it's on our roadmap and under active development.
See the cellxgene extensions section of our documentation for examples of community use and cellxgene extensions.
Security
If you believe you have found a security issue, we would appreciate notification. Please send email to security@chanzuckerberg.com.
Inspiration
We've been heavily inspired by several other related single-cell visualization projects, including the UCSC Cell Browser, Cytoscape, Xena, ASAP, GenePattern, 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