cellxgene 0.11.2

Web application for exploration of large scale scRNA-seq datasets

cellxgene

an interactive explorer for single-cell transcriptomics data

cellxgene (pronounced "sell-by-jean") 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, and to demonstrate general, scalable, and reusable patterns for scientific data visualization.

• Want to install and use cellxgene? Visit the cellxgene docs.
• Want to see where we are going? Check out our roadmap.
• Want to contribute? See our contributors guide.

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

Download an example anndata file

curl -o pbmc3k.h5ad https://raw.githubusercontent.com/chanzuckerberg/cellxgene/master/example-dataset/pbmc3k.h5ad

Launch cellxgene

cellxgene launch pbmc3k.h5ad --open

To learn more about what you can do with cellxgene, see the Getting Started guide.

get in touch

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!

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 [e-mail].

where we are going

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.

• Click install and launch
• Manual annotation workflows
• Toggle embeddings
• Gene information

For more detail on these features and where we are going, see our roadmap.

risks of hosting cellxgene

cellxgene is built on standard web technologies, but is currently designed as a single-user desktop application. We've done this so we can prioritize the features on our roadmap.

Some of our users have experimented with hosting cellxgene either for their lab or for public use, but please note that the cellxgene team does not officially support, troubleshoot, or maintain any web deployments at this time.

If do you choose setup cellxgene as a hosted service, you should be aware of the following risks:

• $ cellxgene launch uses Flask's development server, which is not recommended for hosted deployment (see the Flask documentation)
• We have no testing or official support for deployments where multiple users are accessing the same cellxgene instance.
• Your cellxgene instance is likely to hang or crash if too many people access it at the same time, especially if they using functions that call the Python backend (such as differential expression, updating the layout, or coloring by gene).
• cellxgene only supports one instance per dataset

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

core team

• Colin Megill, frontend & product design
• Charlotte Weaver, software engineer
• Bruce Martin, software engineer
• Sidney Bell, computational biologist
• Justin Kiggins, product manager

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.