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.

• Interactive exploration: select, cross-filter, and compare subsets of your data with performant indexing and data handling.

• 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.