{"id":13688926,"url":"https://github.com/saezlab/decoupler-py","last_synced_at":"2025-05-15T16:04:41.481Z","repository":{"id":41280155,"uuid":"428664507","full_name":"saezlab/decoupler-py","owner":"saezlab","description":"Python package to perform enrichment analysis from omics data.","archived":false,"fork":false,"pushed_at":"2025-04-29T15:42:12.000Z","size":87089,"stargazers_count":192,"open_issues_count":5,"forks_count":28,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-04-29T16:44:10.382Z","etag":null,"topics":["bioinformatics","data-science","enrichment","enrichment-analysis","numba","python","single-cell","spatial-transcriptomics","transcriptomics"],"latest_commit_sha":null,"homepage":"https://decoupler-py.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/saezlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-11-16T13:22:35.000Z","updated_at":"2025-04-20T16:42:58.000Z","dependencies_parsed_at":"2023-02-10T15:30:48.666Z","dependency_job_id":"997e0511-4934-4d02-8a10-219cb58bbc48","html_url":"https://github.com/saezlab/decoupler-py","commit_stats":{"total_commits":486,"total_committers":11,"mean_commits":44.18181818181818,"dds":"0.46502057613168724","last_synced_commit":"f3bc59b48985d8aa361b78a5de2e8cac8cd031eb"},"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fdecoupler-py","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fdecoupler-py/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fdecoupler-py/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Fdecoupler-py/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saezlab","download_url":"https://codeload.github.com/saezlab/decoupler-py/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254374409,"owners_count":22060610,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","data-science","enrichment","enrichment-analysis","numba","python","single-cell","spatial-transcriptomics","transcriptomics"],"created_at":"2024-08-02T15:01:27.732Z","updated_at":"2025-05-15T16:04:41.460Z","avatar_url":"https://github.com/saezlab.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# decoupler - Ensemble of methods to infer biological activities \u003cimg src=\"https://github.com/saezlab/decoupleR/blob/master/inst/figures/logo.svg?raw=1\" align=\"right\" width=\"120\" class=\"no-scaled-link\" /\u003e\n\u003c!-- badges: start --\u003e\n[![main](https://github.com/saezlab/decoupler-py/actions/workflows/ci.yml/badge.svg)](https://github.com/saezlab/decoupler-py/actions)\n[![GitHub issues](https://img.shields.io/github/issues/saezlab/decoupler-py.svg)](https://github.com/saezlab/decoupler-py/issues/)\n[![Documentation Status](https://readthedocs.org/projects/decoupler-py/badge/?version=latest)](https://decoupler-py.readthedocs.io/en/latest/?badge=latest)\n[![codecov](https://codecov.io/gh/saezlab/decoupler-py/branch/main/graph/badge.svg?token=TM0P29KKN5)](https://codecov.io/gh/saezlab/decoupler-py)\n[![Downloads](https://static.pepy.tech/badge/decoupler)](https://pepy.tech/project/decoupler)\n\n[![Conda Recipe](https://img.shields.io/badge/recipe-decoupler--py-green.svg)](https://anaconda.org/conda-forge/decoupler-py)\n[![Conda page](https://img.shields.io/conda/vn/conda-forge/decoupler-py.svg)](https://anaconda.org/conda-forge/decoupler-py)\n[![Conda downloads](https://img.shields.io/conda/dn/conda-forge/decoupler-py.svg)](https://anaconda.org/conda-forge/decoupler-py)\n\u003c!-- badges: end --\u003e\n\n`decoupler` is a package containing different enrichment statistical methods to extract biologically driven scores from omics data within a unified framework.\nThis is its faster and memory efficient Python implementation, for the R version go [here](https://github.com/saezlab/decoupleR).\n\nFor further information and example tutorials, please check our [documentation](https://decoupler-py.readthedocs.io/en/latest/index.html).\n\nIf you have any question or problem do not hesitate to open an [issue](https://github.com/saezlab/decoupler-py/issues).\n\n## Installation\n\n`decoupler` can be installed from `pip` (lightweight installation)::\n```\npip install decoupler\n```\n\nIt can also be installed from `conda` and `mamba` (this includes extra dependencies):\n```\nmamba create -n=decoupler conda-forge::decoupler-py\n```\n\nAlternatively, to stay up-to-date with the newest unreleased version, install from source: \n```\npip install git+https://github.com/saezlab/decoupler-py.git\n```\n\n## scverse\n`decoupler` is part of the [scverse](https://scverse.org) ecosystem, a collection of tools for single-cell omics data analysis in python.\nFor more information check the link.\n\n## License\nEnrichment methods inside decoupler can be used for academic or commercial purposes, except `viper` which holds a non-commercial license. \n\nThe data redistributed by OmniPath does not have a single license, each original resource has its own. By default, `decoupler`\nassumes an academic license, but commercial or nonprofit licenses can be specified in the `license` parameter of `decoupler`'s OmniPath functions.\n[Here](https://omnipathdb.org/info) one can find the license information of all the resources in OmniPath.\n\n## Citation\n\nBadia-i-Mompel P., Vélez Santiago J., Braunger J., Geiss C., Dimitrov D.,\nMüller-Dott S., Taus P., Dugourd A., Holland C.H., Ramirez Flores R.O.\nand Saez-Rodriguez J. 2022. decoupleR: Ensemble of computational methods\nto infer biological activities from omics data. Bioinformatics Advances.\n\u003chttps://doi.org/10.1093/bioadv/vbac016\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Fdecoupler-py","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaezlab%2Fdecoupler-py","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Fdecoupler-py/lists"}