Metadata-Version: 2.1
Name: decoupler
Version: 1.4.0
Summary: Ensemble of methods to infer biological activities from omics data
Home-page: https://github.com/saezlab/decoupler-py
Author: Pau Badia i Mompel
Author-email: pau.badia@uni-heidelberg.de
Project-URL: Bug Tracker, https://github.com/saezlab/decoupler-py/issues
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

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**decoupler** is a package containing different statistical methods to extract biological activities from omics data within a unified framework.
This is its faster and memory efficient Python implementation, for the R version go [here](https://github.com/saezlab/decoupleR).

For further information and example tutorials, please check our [documentation](https://decoupler-py.readthedocs.io/en/latest/index.html).

If you have any question or problem do not hesitate to open an [issue](https://github.com/saezlab/decoupler-py/issues).

## Installation

To install the latest stable version run:
```
pip install decoupler
```

Alternatively, to stay up-to-date with the newest version, run: 
```
pip install git+https://github.com/saezlab/decoupler-py.git
```

## scverse
**decoupler** is part of the [scverse](https://scverse.org) ecosystem, a collection of tools for single-cell omics data analysis in python.
For more information check the link.

## License
Footprint methods inside decoupler can be used for academic or commercial purposes, except `viper` which holds a non-commercial license. 

The data redistributed by OmniPath does not have a license, each original resource carries their own. 
[Here](https://omnipathdb.org/info) one can find the license information of all the resources in OmniPath.

## Citation

Badia-i-Mompel P., Vélez Santiago J., Braunger J., Geiss C., Dimitrov D.,
Müller-Dott S., Taus P., Dugourd A., Holland C.H., Ramirez Flores R.O.
and Saez-Rodriguez J. 2022. decoupleR: Ensemble of computational methods
to infer biological activities from omics data. Bioinformatics Advances.
<https://doi.org/10.1093/bioadv/vbac016>
