decoupler - Ensemble of methods to infer enrichment scores#

decoupler is a python package containing different enrichment statistical methods to extract biologically driven scores from omics data within a unified framework. This is its faster and memory efficient Python implementation, a deprecated version in R can be found here.

decoupler is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. If you like scverse® and want to support our mission, please consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.

Getting started#

Please refer to the documentation, in particular, the API documentation.

Installation#

You need to have Python 3.10 or newer installed on your system. If you don’t have Python installed, we recommend installing uv.

There are several alternative options to install decoupler:

Install the latest stable release from PyPI with minimal dependancies:

pip install decoupler

Install the latest stable full release from PyPI with extra dependancies:

pip install decoupler[full]

Install the latest stable version from conda-forge using mamba or conda (pay attention to the -py suffix at the end):

mamba create -n=dcp conda-forge::decoupler-py

Install the latest development version:

pip install git+https://github.com/scverse/decoupler.git@main

Release notes#

See the changelog.

Contact#

For questions and help requests, you can reach out in the scverse discourse. If you found a bug, please use the issue tracker.

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

decoupler - Ensemble of methods to infer enrichment scores

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