Single-Cell Analysis in Python.
Project description
|Docs| |PyPI| |Build Status|
.. |Docs| image:: https://readthedocs.org/projects/scanpy/badge/?version=latest
:target: https://scanpy.readthedocs.io
.. |PyPI| image:: https://badge.fury.io/py/scanpy.svg
:target: https://pypi-hypernode.com/pypi/scanpy
.. |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
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Scanpy – Single-Cell Analysis in Python
=======================================
.. raw:: html
<p>
<img src="http://falexwolf.de/img/tsne_1.3M.png" style="width: 100px; margin: 10px 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, differential expression testing and simulation of gene
regulatory networks. 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>`_.
.. |Docs| image:: https://readthedocs.org/projects/scanpy/badge/?version=latest
:target: https://scanpy.readthedocs.io
.. |PyPI| image:: https://badge.fury.io/py/scanpy.svg
:target: https://pypi-hypernode.com/pypi/scanpy
.. |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: 100px; margin: 10px 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, differential expression testing and simulation of gene
regulatory networks. 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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