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 3.11 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
-pysuffix 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
Claude Code Skill
decoupler ships an Agent Skill for Claude Code that teaches the agent to use the library correctly. Install it once into your personal skills directory:
decoupler-install-skills
This copies the skill to ~/.claude/skills/decoupler/, making it available in every project (re-run with --force after upgrading). Once installed, just ask Claude Code to do decoupler tasks ("find the most active TFs in this cell cluster", "score hallmark gene sets with ora", "build a consensus across methods") and the skill is consulted automatically. To avoid copying into your home directory, point Claude Code at the bundled copy instead:
export CLAUDE_SKILLS_PATH="$(decoupler-install-skills --print-path)"
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
Release files for decoupler 2.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| decoupler-2.2.0.tar.gz | 1.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| decoupler-2.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.4 MB
Release files / decoupler-2.2.0.tar.gz
| Download URL | decoupler-2.2.0.tar.gz |
|---|---|
| Size | 1.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / decoupler-2.2.0-py3-none-any.whl
| Download URL | decoupler-2.2.0-py3-none-any.whl |
|---|---|
| Size | 147.4 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 11, 2026.
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