Euxhenh Cellar Save

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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

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.. 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

• Cellar Web Server: https://cellar.cmu.hubmapconsortium.org/app/cellar
• Official Source Code Repository: https://github.com/euxhenh/cellar
• Documentation & Tutorials: https://euxhenh.github.io/cellar/
• Video Tutorials: https://www.youtube.com/playlist?list=PL5sLSLkTYpWgfBQ0M8ObfBIqDMAzx0-D2
• Issue Tracker: https://github.com/euxhenh/cellar/issues
• Paper: https://www.nature.com/articles/s41467-022-29744-0
• DOI: https://zenodo.org/badge/latestdoi/372980254

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:[email protected]>__.