Single-Cell Analysis in Python. Scales to >1M cells.
|Stars| |PyPI| |PyPIDownloads| |BiocondaDownloads| |Docs| |Build Status|
.. |Stars| image:: https://img.shields.io/github/stars/theislab/scanpy?logo=GitHub&color=yellow :target: https://github.com/theislab/scanpy/stargazers .. |PyPI| image:: https://img.shields.io/pypi/v/scanpy?logo=PyPI :target: https://pypi.org/project/scanpy .. |PyPIDownloads| image:: https://pepy.tech/badge/scanpy :target: https://pepy.tech/project/scanpy .. doesn't really add anything .. |Bioconda| image:: https://img.shields.io/conda/vn/bioconda/scanpy?logo=Anaconda&color=green :target: https://bioconda.github.io/recipes/scanpy/README.html .. |BiocondaDownloads| image:: https://img.shields.io/conda/dn/bioconda/scanpy?logo=Anaconda&color=green :target: https://bioconda.github.io/recipes/scanpy/README.html .. |Docs| image:: https://readthedocs.com/projects/icb-scanpy/badge/?version=latest :target: https://scanpy.readthedocs.io .. |Build Status| image:: https://dev.azure.com/theislab/scanpy/apis/build/status/theislab.scanpy?branchName=master :target: https://dev.azure.com/theislab/scanpy/build .. wait until we have better coverage ;-) .. |Coverage| image:: https://codecov.io/gh/theislab/scanpy/branch/master/graph/badge.svg :target: https://codecov.io/gh/theislab/scanpy
Scanpy is a scalable toolkit for analyzing single-cell gene expression data built jointly with
anndata__. It includes preprocessing, visualization, clustering, trajectory inference and differential expression testing. The Python-based implementation efficiently deals with datasets of more than one million cells.
Discuss usage on Discourse. Read the documentation. If you'd like to contribute by opening an issue or creating a pull request, please take a look at our
contributing guide. If Scanpy is useful for your research, consider citing
Genome Biology (2018).
.. _Discourse: https://scanpy.discourse.group/ .. _documentation: https://scanpy.readthedocs.io .. _contributing guide: CONTRIBUTING.md .. _Genome Biology (2018): https://doi.org/10.1186/s13059-017-1382-0