pySCENIC
========
|buildstatus|_ |pypipackage|_ |docstatus|_
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.
The pioneering work was done in R and results were published in Nature Methods [1]_.
A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols [4]_.
pySCENIC can be run on a single desktop machine but easily scales to multi-core clusters to analyze thousands of cells
in no time. The latter is achieved via the dask_ framework for distributed computing [2]_.
**Full documentation** for pySCENIC is available on `Read the Docs <https://pyscenic.readthedocs.io/en/latest/>`_
----
pySCENIC is part of the SCENIC Suite of tools!
See the main `SCENIC website <https://scenic.aertslab.org/>`_ for additional information and a full list of tools available.
----
News and releases
-----------------
0.12.0 | 2022-08-16
^^^^^^^^^^^^^^^^^^^
* Only databases in Feather v2 format are supported now (`ctxcore <https://github.com/aertslab/ctxcore>`_ ``>= 0.2``),
which allow uses recent versions of pyarrow (``>=8.0.0``) instead of very old ones (``<0.17``).
Databases in the new format can be downloaded from https://resources.aertslab.org/cistarget/databases/
and end with ``*.genes_vs_motifs.rankings.feather`` or ``*.genes_vs_tracks.rankings.feather``.
* Support clustered motif databases.
* Use custom multiprocessing instead of dask, by default.
* Docker image uses python 3.10 and contains only needed pySCENIC dependencies for CLI usage.
* Remove unneeded scripts and notebooks for unused/deprecated database formats.
0.11.2 | 2021-05-07
^^^^^^^^^^^^^^^^^^^
* Split some core cisTarget functions out into a separate repository, `ctxcore <https://github.com/aertslab/ctxcore>`_. This is now a required package for pySCENIC.
0.11.1 | 2021-02-11
^^^^^^^^^^^^^^^^^^^
* Fix bug in motif url construction (#275)
* Fix for export2loom with sparse dataframe (#278)
* Fix sklearn t-SNE import (#285)
* Updates to Docker image (expose port 8787 for Dask dashboard)
0.11.0 | 2021-02-10
^^^^^^^^^^^^^^^^^^^
**Major features:**
* Updated arboreto_ release (GRN inference step) includes:
* Support for sparse matrices (using the ``--sparse`` flag in ``pyscenic grn``, or passing a sparse matrix to ``grnboost2``/``genie3``).
* Fixes to avoid dask metadata mismatch error
* Updated cisTarget:
* Fix for metadata mismatch in ctx prune2df step
* Support for databases Apache Parquet format
* Faster loading from feather databases
* Bugfix: loading genes from a database (previously missing the last gene name in the database)
* Support for Anndata input and output
* Package updates:
* Upgrade to newer pandas version
* Upgrade to newer numba version
* Upgrade to newer versions of dask, distributed
* Input checks and more descriptive error messages.
* Check that regulons loaded are not empty.
* Bugfixes:
* In the regulons output from the cisTarget step, the gene weights were incorrectly assigned to their respective target genes (PR #254).
* Motif url construction fixed when running ctx without pruning
* Compression of intermediate files in the CLI steps
* Handle loom files with non-standard gene/cell attribute names
* Reformat the genesig gmt input/output
* Fix AUCell output to loom with non-standard loom attributes
0.10.4 | 2020-11-24
^^^^^^^^^^^^^^^^^^^
* Included new CLI option to add correlation information to the GRN adjacencies file. This can be called with ``pyscenic add_cor``.
See also the extended `Release Notes <https://pyscenic.readthedocs.io/en/latest/releasenotes.html>`_.
Overview
--------
The pipeline has three steps:
1. 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.
2. 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.
3. Finally, the original cells are differentiated and clustered on the activity of these discovered regulons.
The 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.
All the functionality of the original R implementation is available and in addition:
1. You can leverage multi-core and multi-node clusters using dask_ and its distributed_ scheduler.
2. We implemented a version of the recovery of input genes that takes into account weights associated with these genes.
3. 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.
Additional resources
--------------------
For more information, please visit LCB_,
the main `SCENIC website <https://scenic.aertslab.org/>`_,
or `SCENIC (R version) <https://github.com/aertslab/SCENIC>`_.
There is a tutorial to `create new cisTarget databases <https://github.com/aertslab/create_cisTarget_databases>`_.
The CLI to pySCENIC has also been streamlined into a pipeline that can be run with a single command, using the Nextflow workflow manager.
There are two Nextflow implementations available:
* `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.
* `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).
Acknowledgments
---------------
We 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).
References
----------
.. [1] Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017). `doi:10.1038/nmeth.4463 <https://doi.org/10.1038/nmeth.4463>`_
.. [2] Rocklin, M. Dask: parallel computation with blocked algorithms and task scheduling. conference.scipy.org
.. [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 <https://doi.org/10.1371/journal.pone.0012776>`_
.. [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 <https://doi.org/10.1038/s41596-020-0336-2>`_
.. |buildstatus| image:: https://travis-ci.org/aertslab/pySCENIC.svg?branch=master
.. _buildstatus: https://travis-ci.org/aertslab/pySCENIC
.. |pypipackage| image:: https://img.shields.io/pypi/v/pySCENIC?color=%23026aab
.. _pypipackage: https://pypi.org/project/pyscenic/
.. |docstatus| image:: https://readthedocs.org/projects/pyscenic/badge/?version=latest
.. _docstatus: http://pyscenic.readthedocs.io/en/latest/?badge=latest
.. _SCENIC: http://scenic.aertslab.org
.. _dask: https://dask.pydata.org/en/latest/
.. _distributed: https://distributed.readthedocs.io/en/latest/
.. _arboreto: https://arboreto.readthedocs.io
.. _LCB: https://aertslab.org
.. _`SCENICprotocol`: https://github.com/aertslab/SCENICprotocol
.. _`VSNPipelines`: https://github.com/vib-singlecell-nf/vsn-pipelines
.. _notebooks: https://github.com/aertslab/pySCENIC/tree/master/notebooks
.. _issue: https://github.com/aertslab/pySCENIC/issues/new
.. _PyPI: https://pypi.python.org/pypi/pyscenic
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"description": "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. 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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 <https://pyscenic.readthedocs.io/en/latest/releasenotes.html>`_.\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 <https://scenic.aertslab.org/>`_,\nor `SCENIC (R version) <https://github.com/aertslab/SCENIC>`_.\nThere is a tutorial to `create new cisTarget databases <https://github.com/aertslab/create_cisTarget_databases>`_.\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\u20131086 (2017). `doi:10.1038/nmeth.4463 <https://doi.org/10.1038/nmeth.4463>`_\n.. [2] Rocklin, M. Dask: parallel computation with blocked algorithms and task scheduling. conference.scipy.org\n.. 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