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cellxgene
An interactive, performant explorer for single cell transcriptomics data.
cellxgene is an open-source experiment in how to bring powerful tools from modern web development to visualize and explore large single-cell transcriptomics datasets.
Started in the context of the Human Cell Atlas Consortium, cellxgene hopes to both enable scientists to explore their data and to equip developers with scalable, reusable patterns and frameworks for visualizing large scientific datasets.
Features
-
Visualization at scale: built with WebGL, React & Redux to handle visualization of at least 1 million cells.
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Interactive exploration: select, cross-filter, and compare subsets of your data with performant indexing and data handling.
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Flexible API: the cellxgene client-server model is designed to support a range of existing analysis packages for backend computational tasks (eg scanpy), integrated with client-side visualization via a REST API.
Getting Started
Requirements
- OS: OSX, Windows, Linux
- python 3.6
- npm
- Google Chrome
Clone project
git clone https://github.com/chanzuckerberg/cellxgene.git
Install client
cd cellxgene
./bin/build-client
To use with virtual env for python
(optional, but recommended)
ENV_NAME=cellxgene
python3 -m venv ${ENV_NAME}
source ${ENV_NAME}/bin/activate
Install server
python3 setup.py install
Run (with demo data)
cellxgene --title PBMC3K scanpy example-dataset/
In google chrome, navigate to the viewer via the web address printed in your console.
E.g., Running on http://0.0.0.0:5005/
Help
cellxgene --help
For help with the scanpy engine
cellxgene scanpy --help
Contributing
We warmly welcome contributions from the community. Please submit any bug reports and feature requests through github issues. Please submit any direct contributions via a branch + pull request.
Inspiration and collaboration
We’ve been inspired by several other related efforts in this space, including the UCSC Cell Browswer, Cytoscape, Xena, ASAP, Gene Pattern, & many others; we hope to explore collaborations where useful.
Reuse
This project was started with the sole goal of empowering the scientific community to explore and understand their data. As such, we whole-heartedly encourage other scientific tool builders to adopt the patterns, tools, and code from this project, and reach out to us with ideas or questions using Github Issues or Pull Requests. All code is freely available for reuse under the MIT license.
We thank Alex Wolf for the demo dataset.