Cellar is an interactive tool for analyzing single-cell omics data. Cellar is built in Python using the Dash framework and relies on several open-source packages.
The app is developed and actively maintained by the Systems Biology Group at Carnegie Mellon University. Our web-server running Cellar can be accessed here. See below for a local installation.
An accompanying preprint and supplementary files can be accessed through bioRxiv.
The documentation 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 are also available.
- Cellar Web Server: https://data.test.hubmapconsortium.org/app/cellar
- Official Source Code Repository: https://github.com/ferrocactus/CellarV
- Documentation & Tutorials: https://ferrocactus.github.io/CellarV/
- Video Tutorials: https://www.youtube.com/playlist?list=PL5sLSLkTYpWgfBQ0M8ObfBIqDMAzx0-D2
- Issue Tracker: https://github.com/ferrocactus/CellarV/issues
- Preprint: https://www.biorxiv.org/content/10.1101/2021.03.19.436162v1?rss=1
Probably the easiest way to install Cellar locally is using Docker.
The image name is euxhen/cellarv and can be pulled with:
docker pull euxhen/cellarv
After the pull is complete, running Cellar is as simple as:
docker run --rm -p 8050:8050 euxhen/cellarv
and visiting localhost:8050 on your web browser.
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.
Cellar's source code can be downloaded using:
git clone https://github.com/ferrocactus/CellarV
Cellar requires the following Python packages:
- dash (== 1.21.0), dash-bootstrap-components (== 0.13.0), dash-bio (>= 0.7.1)
- numpy (>= 1.21.1), scipy (>= 1.6.3)
- scikit-learn (>= 0.24.2), scikit-learn-extra (>= 0.2.0), scikit-image (>= 0.18.1)
- pandas (>= 1.3.1), anndata (>= 0.7.6), scanpy (>= 1.8.1)
- matplotlib (>= 3.4.2), plotly (>= 5.1.0), python-kaleido (>= 0.2.1)
- tifffile (>= 2019.7.26.2)
- igraph (>= 0.9.4), leidenalg (>= 0.8.7)
- umap-learn (>= 0.5.1)
- faiss-cpu (>= 1.7.1)
- joblib (>= 1.0.1)
- zodb (>= 5.6.0)
- r-base (>= 4.1.0), r-stringi (>= 1.7.3), rpy2 (>= 3.4.5), anndata2ri (>= 1.0.6)
- pydiffmap (>= 0.2.0.1)
- diffxpy (>= 0.7.4), gseapy (>= 0.10.5), pyensembl (>=1.9.4), bintogene (>= 1.23)
- kneed (>= 0.7.0)
and the following R packages
- SingleR (>= 1.6.1)
- aertslab/cisTopic (>= 0.3.0)
- STvEA (>= 0.2.0)
These R packages can be installed using the provided python script
install_Rdeps.py.
The entry point of the application is main.py and can be run with:
python main.py
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.