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.. |PythonVersion| image:: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue .. _PythonVersion: https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue .. |MITLicense| image:: https://img.shields.io/badge/License-MIT-blue .. _MITLicense: https://raw.githubusercontent.com/euxhenh/cellar/main/LICENSE.txt .. |Website| image:: https://img.shields.io/website-up-down-green-red/http/shields.io .. _Website: https://cellar.cmu.hubmapconsortium.org/app/cellar .. |DOI| image:: https://zenodo.org/badge/372980254.svg .. _DOI: https://zenodo.org/badge/latestdoi/372980254
.. |PythonMinVersion| replace:: 3.7
.. image:: https://raw.githubusercontent.com/euxhenh/cellar/main/assets/cellar-logo.png :width: 400 :target: https://cellar.cmu.hubmapconsortium.org/app/cellar
Cellar is an interactive tool for analyzing single-cell omics data. Cellar
is built in Python using the Dash <https://plotly.com/dash/>
__ framework
and relies on several open-source packages.
The app is developed and actively maintained by the
Systems Biology Group <http://www.sb.cs.cmu.edu/>
at
Carnegie Mellon University <https://www.cmu.edu/>
. Our web-server
running Cellar can be accessed
here <https://cellar.cmu.hubmapconsortium.org/app/cellar>
__. See below
for a local installation.
An accompanying paper and supplementary files can be accessed via
Nature Communications <https://www.nature.com/articles/s41467-022-29744-0>
__.
The documentation <https://euxhenh.github.io/cellar/>
includes details on how to use Cellar and the data types
it supports. These include but are not limited to scRNA-seq, scATAC-seq,
CODEX, SNARE-seq, sciRNA-seq, Visium. Cellar supports preprocessing,
dimensionality reduction, clustering, DE gene testing, enrichment analysis,
cluster and gene visualization modules, projection to spatial tiles,
label transfer, and semi-supervised clustering among others. The documentation
also contains several written tutorials.
Video tutorials <https://www.youtube.com/playlist?list=PL5sLSLkTYpWgfBQ0M8ObfBIqDMAzx0-D2>
are also available.
Links
Local Installation
Docker Installation
Probably the easiest way to install Cellar locally is using ``Docker``.
The image name is ``euxhen/cellar`` and can be pulled with::
docker pull euxhen/cellar
After the pull is complete, running Cellar is as simple as::
docker run --rm -p 8050:8050 euxhen/cellar
and visiting ``localhost:8050`` on your web browser.
Manual Installation
A manual installation involves cloning the Cellar repository and installing
the necessary Python and R packages. To run Cellar you will need at least
Python 3.7 and R 4.0. We recommend using a Conda
environment
for installing the dependencies. For a full list of dependencies and
installation instructions please refer to the
documentation <https://euxhenh.github.io/cellar/>
__.
Citation
If you use Cellar in your work, we would appreciate citations to Cellar's paper
.. code-block::
@article{Hasanaj2022,
author = {Euxhen Hasanaj and Jingtao Wang and Arjun Sarathi and Jun Ding and Ziv Bar-Joseph},
issn = {2041-1723},
issue = {1},
journal = {Nature Communications 2022 13:1},
month = {4},
pages = {1-6},
publisher = {Nature Publishing Group},
title = {Interactive single-cell data analysis using Cellar},
volume = {13},
year = {2022},
}
Contributing
We welcome code contributions as well as feature requests. To request
new features please raise an issue in the links provided above or directly
send us an email <mailto:ehasanaj@cs.cmu.edu>
__.