{"id":47472803,"url":"https://github.com/aertslab/pySCENIC","last_synced_at":"2026-04-08T18:00:38.032Z","repository":{"id":38291162,"uuid":"124243757","full_name":"aertslab/pySCENIC","owner":"aertslab","description":"pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.","archived":false,"fork":false,"pushed_at":"2025-06-26T09:57:37.000Z","size":36493,"stargazers_count":594,"open_issues_count":238,"forks_count":207,"subscribers_count":17,"default_branch":"master","last_synced_at":"2026-04-03T19:54:00.687Z","etag":null,"topics":["gene-regulatory-network","single-cell","transcription-factors","transcriptomics"],"latest_commit_sha":null,"homepage":"http://scenic.aertslab.org","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/aertslab.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.txt","code_of_conduct":"CODE_OF_CONDUCT.md","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,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2018-03-07T13:57:49.000Z","updated_at":"2026-04-01T11:03:07.000Z","dependencies_parsed_at":"2025-09-08T15:00:40.893Z","dependency_job_id":"a8bad455-67d1-4c23-ab99-e5f1a7aea9bb","html_url":"https://github.com/aertslab/pySCENIC","commit_stats":{"total_commits":613,"total_committers":18,"mean_commits":34.05555555555556,"dds":0.3849918433931484,"last_synced_commit":"31d51a1625f12fb3c6e92bc48ecc9d401524c22a"},"previous_names":[],"tags_count":79,"template":false,"template_full_name":null,"purl":"pkg:github/aertslab/pySCENIC","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aertslab%2FpySCENIC","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aertslab%2FpySCENIC/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aertslab%2FpySCENIC/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aertslab%2FpySCENIC/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aertslab","download_url":"https://codeload.github.com/aertslab/pySCENIC/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aertslab%2FpySCENIC/sbom","scorecard":{"id":169193,"data":{"date":"2025-08-11","repo":{"name":"github.com/aertslab/pySCENIC","commit":"06bafba412792f6efa5a552a23bb221cc3bdea1b"},"scorecard":{"version":"v5.2.1-40-gf6ed084d","commit":"f6ed084d17c9236477efd66e5b258b9d4cc7b389"},"score":2,"checks":[{"name":"Code-Review","score":0,"reason":"Found 0/30 approved changesets -- score normalized to 0","details":null,"documentation":{"short":"Determines if the project requires human code review before pull requests (aka merge requests) are merged.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#code-review"}},{"name":"Packaging","score":-1,"reason":"packaging workflow not detected","details":["Warn: no GitHub/GitLab publishing workflow detected."],"documentation":{"short":"Determines if the project is published as a package that others can easily download, install, easily update, and uninstall.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#packaging"}},{"name":"Token-Permissions","score":-1,"reason":"No tokens found","details":null,"documentation":{"short":"Determines if the project's workflows follow the principle of least privilege.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#token-permissions"}},{"name":"Maintained","score":3,"reason":"0 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 3","details":null,"documentation":{"short":"Determines if the project is \"actively maintained\".","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#maintained"}},{"name":"Binary-Artifacts","score":10,"reason":"no binaries found in the repo","details":null,"documentation":{"short":"Determines if the project has generated executable (binary) artifacts in the source repository.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#binary-artifacts"}},{"name":"SAST","score":0,"reason":"no SAST tool detected","details":["Warn: no pull requests merged into dev branch"],"documentation":{"short":"Determines if the project uses static code analysis.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#sast"}},{"name":"Dangerous-Workflow","score":-1,"reason":"no workflows found","details":null,"documentation":{"short":"Determines if the project's GitHub Action workflows avoid dangerous patterns.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#dangerous-workflow"}},{"name":"CII-Best-Practices","score":0,"reason":"no effort to earn an OpenSSF best practices badge detected","details":null,"documentation":{"short":"Determines if the project has an OpenSSF (formerly CII) Best Practices Badge.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#cii-best-practices"}},{"name":"Security-Policy","score":0,"reason":"security policy file not detected","details":["Warn: no security policy file detected","Warn: no security file to analyze","Warn: no security file to analyze","Warn: no security file to analyze"],"documentation":{"short":"Determines if the project has published a security policy.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#security-policy"}},{"name":"Fuzzing","score":0,"reason":"project is not fuzzed","details":["Warn: no fuzzer integrations found"],"documentation":{"short":"Determines if the project uses fuzzing.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#fuzzing"}},{"name":"License","score":10,"reason":"license file detected","details":["Info: project has a license file: LICENSE.txt:0","Info: FSF or OSI recognized license: GNU General Public License v3.0: LICENSE.txt:0"],"documentation":{"short":"Determines if the project has defined a license.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#license"}},{"name":"Signed-Releases","score":-1,"reason":"no releases found","details":null,"documentation":{"short":"Determines if the project cryptographically signs release artifacts.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#signed-releases"}},{"name":"Pinned-Dependencies","score":0,"reason":"dependency not pinned by hash detected -- score normalized to 0","details":["Warn: containerImage not pinned by hash: Dockerfile:1","Warn: containerImage not pinned by hash: Dockerfile:29","Warn: containerImage not pinned by hash: pyscenic_with_scanpy.Dockerfile:1","Warn: containerImage not pinned by hash: pyscenic_with_scanpy.Dockerfile:14","Warn: pipCommand not pinned by hash: Dockerfile:17","Warn: pipCommand not pinned by hash: Dockerfile:25-27","Warn: pipCommand not pinned by hash: pyscenic_with_scanpy.Dockerfile:12","Info:   0 out of   4 containerImage dependencies pinned","Info:   0 out of   3 pipCommand dependencies pinned"],"documentation":{"short":"Determines if the project has declared and pinned the dependencies of its build process.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#pinned-dependencies"}},{"name":"Branch-Protection","score":0,"reason":"branch protection not enabled on development/release branches","details":["Warn: branch protection not enabled for branch 'master'"],"documentation":{"short":"Determines if the default and release branches are protected with GitHub's branch protection settings.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#branch-protection"}},{"name":"Vulnerabilities","score":0,"reason":"20 existing vulnerabilities detected","details":["Warn: Project is vulnerable to: PYSEC-2021-387 / PYSEC-2021-871 / PYSEC-2021-872 / GHSA-hwqr-f3v9-hwxr / GHSA-j8fq-86c5-5v2r","Warn: Project is vulnerable to: PYSEC-2023-163 / GHSA-f73w-4m7g-ch9x","Warn: Project is vulnerable to: PYSEC-2018-34 / GHSA-2fc2-6r4j-p65h","Warn: Project is vulnerable to: PYSEC-2021-856 / GHSA-5545-2q6w-2gh6","Warn: Project is vulnerable to: PYSEC-2019-108 / GHSA-9fq2-x9r6-wfmf","Warn: Project is vulnerable to: PYSEC-2018-33 / GHSA-cw6w-4rcx-xphc","Warn: Project is vulnerable to: PYSEC-2021-857 / GHSA-f7c7-j99h-c22f","Warn: Project is vulnerable to: GHSA-fpfv-jqm9-f5jm","Warn: Project is vulnerable to: PYSEC-2017-1 / GHSA-frgw-fgh6-9g52","Warn: Project is vulnerable to: PYSEC-2020-107 / GHSA-jjw5-xxj6-pcv5","Warn: Project is vulnerable to: PYSEC-2024-110 / GHSA-jw8x-6495-233v","Warn: Project is vulnerable to: PYSEC-2020-108","Warn: Project is vulnerable to: PYSEC-2019-156 / GHSA-xp76-357g-9wqq","Warn: Project is vulnerable to: PYSEC-2023-102","Warn: Project is vulnerable to: PYSEC-2023-114","Warn: Project is vulnerable to: PYSEC-2013-22 / GHSA-27x4-j476-jp5f","Warn: Project is vulnerable to: PYSEC-2025-49 / GHSA-5rjg-fvgr-3xxf","Warn: Project is vulnerable to: GHSA-cx63-2mw6-8hw5","Warn: Project is vulnerable to: PYSEC-2022-43012 / GHSA-r9hx-vwmv-q579","Warn: Project is vulnerable to: PYSEC-2017-74"],"documentation":{"short":"Determines if the project has open, known unfixed vulnerabilities.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#vulnerabilities"}}]},"last_synced_at":"2025-08-16T15:55:04.754Z","repository_id":38291162,"created_at":"2025-08-16T15:55:04.754Z","updated_at":"2025-08-16T15:55:04.754Z"},"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31567227,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["gene-regulatory-network","single-cell","transcription-factors","transcriptomics"],"created_at":"2026-03-25T08:00:37.370Z","updated_at":"2026-04-08T18:00:38.026Z","avatar_url":"https://github.com/aertslab.png","language":"Python","funding_links":[],"categories":["Gene Regulatory Network Inference"],"sub_categories":["Single-Cell Methods"],"readme":"pySCENIC\n========\n\n|buildstatus|_ |pypipackage|_ |docstatus|_\n\n\npySCENIC is a lightning-fast python implementation of the SCENIC_ pipeline (Single-Cell rEgulatory Network Inference and\nClustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from\nsingle-cell RNA-seq data.\n\nThe pioneering work was done in R and results were published in Nature Methods [1]_.\nA new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols [4]_.\n\npySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells\nin no time. The latter is achieved via the dask_ framework for distributed computing [2]_.\n\n**Full documentation** for pySCENIC is available on `Read the Docs \u003chttps://pyscenic.readthedocs.io/en/latest/\u003e`_\n\n----\n\npySCENIC is part of the SCENIC Suite of tools! \nSee the main `SCENIC website \u003chttps://scenic.aertslab.org/\u003e`_ for additional information and a full list of tools available.\n\n----\n\n\nNews and releases\n-----------------\n\n0.12.1 | 2022-11-21\n^^^^^^^^^^^^^^^^^^^\n\n* Add support for running arboreto_with_multiprocessing.py with spawn instead of fork as multiprocessing method.Pool\n* Use ravel instead of flatten to avoid unnecessary memory copy in aucell\n* Update Docker image file and add separated Docker file for pySCENIC with scanpy.\n\n0.12.0 | 2022-08-16\n^^^^^^^^^^^^^^^^^^^\n\n* Only databases in Feather v2 format are supported now (`ctxcore \u003chttps://github.com/aertslab/ctxcore\u003e`_ ``\u003e= 0.2``),\n  which allow uses recent versions of pyarrow (``\u003e=8.0.0``) instead of very old ones (``\u003c0.17``).\n  Databases in the new format can be downloaded from https://resources.aertslab.org/cistarget/databases/\n  and end with ``*.genes_vs_motifs.rankings.feather`` or ``*.genes_vs_tracks.rankings.feather``.\n* Support clustered motif databases.\n* Use custom multiprocessing instead of dask, by default.\n* Docker image uses python 3.10 and contains only needed pySCENIC dependencies for CLI usage.\n* Remove unneeded scripts and notebooks for unused/deprecated database formats.\n\n0.11.2 | 2021-05-07\n^^^^^^^^^^^^^^^^^^^\n\n* Split some core cisTarget functions out into a separate repository, `ctxcore \u003chttps://github.com/aertslab/ctxcore\u003e`_. This is now a required package for pySCENIC.\n\n0.11.1 | 2021-02-11\n^^^^^^^^^^^^^^^^^^^\n\n* Fix bug in motif url construction (#275)\n* Fix for export2loom with sparse dataframe (#278)\n* Fix sklearn t-SNE import (#285)\n* Updates to Docker image (expose port 8787 for Dask dashboard)\n\n0.11.0 | 2021-02-10\n^^^^^^^^^^^^^^^^^^^\n\n**Major features:**\n\n* Updated arboreto_ release (GRN inference step) includes:\n\n  * Support for sparse matrices (using the ``--sparse`` flag in ``pyscenic grn``, or passing a sparse matrix to ``grnboost2``/``genie3``).\n  * Fixes to avoid dask metadata mismatch error\n\n* Updated cisTarget:\n\n  * Fix for metadata mismatch in ctx prune2df step\n  * Support for databases Apache Parquet format\n  * Faster loading from feather databases\n  * Bugfix: loading genes from a database (previously missing the last gene name in the database)\n\n* Support for Anndata input and output\n\n* Package updates:\n\n  * Upgrade to newer pandas version\n  * Upgrade to newer numba version\n  * Upgrade to newer versions of dask, distributed\n\n* Input checks and more descriptive error messages.\n\n  * Check that regulons loaded are not empty.\n\n* Bugfixes:\n\n  * In the regulons output from the cisTarget step, the gene weights were incorrectly assigned to their respective target genes (PR #254).\n  * Motif url construction fixed when running ctx without pruning\n  * Compression of intermediate files in the CLI steps\n  * Handle loom files with non-standard gene/cell attribute names\n  * Reformat the genesig gmt input/output\n  * Fix AUCell output to loom with non-standard loom attributes\n\n\n0.10.4 | 2020-11-24\n^^^^^^^^^^^^^^^^^^^\n\n* Included new CLI option to add correlation information to the GRN adjacencies file. This can be called with ``pyscenic add_cor``.\n\n\n\nSee also the extended `Release Notes \u003chttps://pyscenic.readthedocs.io/en/latest/releasenotes.html\u003e`_.\n\nOverview\n--------\n\nThe pipeline has three steps:\n\n1. First transcription factors (TFs) and their target genes, together defining a regulon, are derived using gene inference methods which solely rely on correlations between expression of genes across cells. The arboreto_ package is used for this step.\n2. These regulons are refined by pruning targets that do not have an enrichment for a corresponding motif of the TF effectively separating direct from indirect targets based on the presence of cis-regulatory footprints.\n3. Finally, the original cells are differentiated and clustered on the activity of these discovered regulons.\n\nThe most impactful speed improvement is introduced by the arboreto_ package in step 1. This package provides an alternative to GENIE3 [3]_ called GRNBoost2. This package can be controlled from within pySCENIC.\n\n\nAll the functionality of the original R implementation is available and in addition:\n\n1. You can leverage multi-core and multi-node clusters using dask_ and its distributed_ scheduler.\n2. We implemented a version of the recovery of input genes that takes into account weights associated with these genes.\n3. Regulons, i.e. the regulatory network that connects a TF with its target genes, with targets that are repressed are now also derived and used for cell enrichment analysis.\n\n\nAdditional resources\n--------------------\n\nFor more information, please visit LCB_, \nthe main `SCENIC website \u003chttps://scenic.aertslab.org/\u003e`_,\nor `SCENIC (R version) \u003chttps://github.com/aertslab/SCENIC\u003e`_.\nThere is a tutorial to `create new cisTarget databases \u003chttps://github.com/aertslab/create_cisTarget_databases\u003e`_.\nThe CLI to pySCENIC has also been streamlined into a pipeline that can be run with a single command, using the Nextflow workflow manager.\nThere are two Nextflow implementations available:\n\n* `SCENICprotocol`_: A Nextflow DSL1 implementation of pySCENIC alongside a basic \"best practices\" expression analysis. Includes details on pySCENIC installation, usage, and downstream analysis, along with detailed tutorials.\n* `VSNPipelines`_: A Nextflow DSL2 implementation of pySCENIC with a comprehensive and customizable pipeline for expression analysis. Includes additional pySCENIC features (multi-runs, integrated motif- and track-based regulon pruning, loom file generation).\n\n\nAcknowledgments\n---------------\n\nWe are grateful to all providers of TF-annotated position weight matrices, in particular Martha Bulyk (UNIPROBE), Wyeth Wasserman and Albin Sandelin (JASPAR), BioBase (TRANSFAC), Scot Wolfe and Michael Brodsky (FlyFactorSurvey) and Timothy Hughes (cisBP).\n\n\nReferences\n----------\n\n.. [1] Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017). `doi:10.1038/nmeth.4463 \u003chttps://doi.org/10.1038/nmeth.4463\u003e`_\n.. [2] Rocklin, M. Dask: parallel computation with blocked algorithms and task scheduling. conference.scipy.org\n.. [3] Huynh-Thu, V. A. et al. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5, (2010). `doi:10.1371/journal.pone.0012776 \u003chttps://doi.org/10.1371/journal.pone.0012776\u003e`_\n.. [4] Van de Sande B., Flerin C., et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. June 2020:1-30. `doi:10.1038/s41596-020-0336-2 \u003chttps://doi.org/10.1038/s41596-020-0336-2\u003e`_\n\n.. |buildstatus| image:: https://travis-ci.org/aertslab/pySCENIC.svg?branch=master\n.. _buildstatus: https://travis-ci.org/aertslab/pySCENIC\n\n.. |pypipackage| image:: https://img.shields.io/pypi/v/pySCENIC?color=%23026aab\n.. _pypipackage: https://pypi.org/project/pyscenic/\n\n.. |docstatus| image:: https://readthedocs.org/projects/pyscenic/badge/?version=latest\n.. _docstatus: http://pyscenic.readthedocs.io/en/latest/?badge=latest\n\n.. _SCENIC: http://scenic.aertslab.org\n.. _dask: https://dask.pydata.org/en/latest/\n.. _distributed: https://distributed.readthedocs.io/en/latest/\n.. _arboreto: https://arboreto.readthedocs.io\n.. _LCB: https://aertslab.org\n.. _`SCENICprotocol`: https://github.com/aertslab/SCENICprotocol\n.. _`VSNPipelines`: https://github.com/vib-singlecell-nf/vsn-pipelines\n.. _notebooks: https://github.com/aertslab/pySCENIC/tree/master/notebooks\n.. _issue: https://github.com/aertslab/pySCENIC/issues/new\n.. _PyPI: https://pypi.python.org/pypi/pyscenic\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faertslab%2FpySCENIC","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faertslab%2FpySCENIC","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faertslab%2FpySCENIC/lists"}