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.
To install cellxgene you need Python 3.6+. We recommend installing cellxgene into a conda or virtual environment.
Install the package.
pip install cellxgene
Download an example anndata file
curl -O https://cellxgene-example-data.czi.technology/pbmc3k.h5ad.zip unzip pbmc3k.h5ad
cellxgene launch pbmc3k.h5ad --open
To learn more about what you can do with cellxgene, see the Getting Started guide.
Have questions, suggestions, or comments? You can come hang out with us by joining the CZI Science Slack and posting in the
#cellxgene-users channel. Have feature requests or bugs? Please submit these as Github issues. We'd love to hear from you!
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.
The current core team:
We would also like to gratefully acknowledge contributions from past core team members:
Our goal is to enable teams of computational and experimental biologists to collaboratively gain insight into their single-cell RNA-seq data.
There are 4 key features we plan to implement in the near term.
For more detail on these features and where we are going, see our roadmap.
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.
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.