Single-Cell Analysis in Python.
Project description
|Docs| |PyPI| |Build Status| |Coverage|
.. |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>`_.
.. |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>`_.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
scanpy-0.4.3.tar.gz
(225.6 kB
view details)
Built Distributions
File details
Details for the file scanpy-0.4.3.tar.gz
.
File metadata
- Download URL: scanpy-0.4.3.tar.gz
- Upload date:
- Size: 225.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 97c2cbcf95cc4857f1ad5390329629c6e40faf9dfee7efcc671beb78eb3dbf60 |
|
MD5 | b624697fb456634ebaa381c0e82054ac |
|
BLAKE2b-256 | e69408a022ff79649d255bf5d906f045a9859e7d1f73b78b2b84ecaabafd1c87 |
File details
Details for the file scanpy-0.4.3-cp36-cp36m-macosx_10_12_x86_64.whl
.
File metadata
- Download URL: scanpy-0.4.3-cp36-cp36m-macosx_10_12_x86_64.whl
- Upload date:
- Size: 382.0 kB
- Tags: CPython 3.6m, macOS 10.12+ x86-64
- Uploaded using Trusted Publishing? No
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 52883a7694cfb9eb3cb2d64ca18ea279869b1d9902857d3fdf5ab5144269e4fa |
|
MD5 | f3f7913a1f12f0f6969f929c78505327 |
|
BLAKE2b-256 | aa12925855d646c0fae26bd29096e1c249a933036f840c348dc634c5c511c8d7 |
File details
Details for the file scanpy-0.4.3-cp35-cp35m-macosx_10_6_x86_64.whl
.
File metadata
- Download URL: scanpy-0.4.3-cp35-cp35m-macosx_10_6_x86_64.whl
- Upload date:
- Size: 390.2 kB
- Tags: CPython 3.5m, macOS 10.6+ x86-64
- Uploaded using Trusted Publishing? No
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ce1b4be1f485f79c34e33c565a7cdd5abb59f693c698b2079480d367eaba2eea |
|
MD5 | 55f51545d94e5c3be2775fd87e19d21f |
|
BLAKE2b-256 | 2f5cca78a4923a13b4a8b4c671b4a8b1d1ecf767b0061b2c4bef1d471f4d2fad |