Single-Cell Analysis in Python.
Project description
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.. |PyPI| image:: https://img.shields.io/pypi/v/scanpy.svg
:target: https://pypi-hypernode.com/project/scanpy
.. |Docs| image:: https://readthedocs.org/projects/scanpy/badge/?version=latest
:target: https://scanpy.readthedocs.io
.. |Build Status| image:: https://travis-ci.org/theislab/scanpy.svg?branch=master
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..
.. |Coverage| image:: https://codecov.io/gh/theislab/scanpy/branch/master/graph/badge.svg
:target: https://codecov.io/gh/theislab/scanpy
Scanpy – Single-Cell Analysis in Python
=======================================
.. raw:: html
<p>
<img src="http://falexwolf.de/img/tsne_1.3M.png" style="width: 90px; margin: 3px 10px 5px 5px" align="left">
Scanpy is a scalable toolkit for analyzing single-cell gene expression
data. It includes preprocessing, visualization, clustering, pseudotime and
trajectory inference and differential expression testing. The Python-based
implementation efficiently deals with datasets of more than one million
cells.
</p>
Read the `documentation <https://scanpy.readthedocs.io>`_.
If Scanpy is useful for your research, consider citing `Genome Biology (2018) <https://doi.org/10.1186/s13059-017-1382-0>`_.
.. |PyPI| image:: https://img.shields.io/pypi/v/scanpy.svg
:target: https://pypi-hypernode.com/project/scanpy
.. |Docs| image:: https://readthedocs.org/projects/scanpy/badge/?version=latest
:target: https://scanpy.readthedocs.io
.. |Build Status| image:: https://travis-ci.org/theislab/scanpy.svg?branch=master
:target: https://travis-ci.org/theislab/scanpy
..
.. |Coverage| image:: https://codecov.io/gh/theislab/scanpy/branch/master/graph/badge.svg
:target: https://codecov.io/gh/theislab/scanpy
Scanpy – Single-Cell Analysis in Python
=======================================
.. raw:: html
<p>
<img src="http://falexwolf.de/img/tsne_1.3M.png" style="width: 90px; margin: 3px 10px 5px 5px" align="left">
Scanpy is a scalable toolkit for analyzing single-cell gene expression
data. It includes preprocessing, visualization, clustering, pseudotime and
trajectory inference and differential expression testing. The Python-based
implementation efficiently deals with datasets of more than one million
cells.
</p>
Read the `documentation <https://scanpy.readthedocs.io>`_.
If Scanpy is useful for your research, consider citing `Genome Biology (2018) <https://doi.org/10.1186/s13059-017-1382-0>`_.
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