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.
|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 . A new and comprehensive description of this Python implementation of the SCENIC pipeline is available in Nature Protocols .
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 _.
Full documentation for pySCENIC is available on
Read the Docs_
pySCENIC is part of the SCENIC Suite of tools! See the main
SCENIC website_ for additional information and a full list of tools available.
0.11.2 | 2021-05-07 ^^^^^^^^^^^^^^^^^^^
ctxcore_. This is now a required package for pySCENIC.
0.11.1 | 2021-02-11 ^^^^^^^^^^^^^^^^^^^
0.11.0 | 2021-02-10 ^^^^^^^^^^^^^^^^^^^
Updated arboreto_ release (GRN inference step) includes:
pyscenic grn, or passing a sparse matrix to
Support for Anndata input and output
Input checks and more descriptive error messages.
0.10.4 | 2020-11-24 ^^^^^^^^^^^^^^^^^^^
See also the extended
The pipeline has three steps:
The most impactful speed improvement is introduced by the arboreto_ package in step 1. This package provides an alternative to GENIE3 _ called GRNBoost2. This package can be controlled from within pySCENIC.
All the functionality of the original R implementation is available and in addition:
For more information, please visit LCB, the main
SCENIC website, or
SCENIC (R version). There is a tutorial to `create new cisTarget databases <https://github.com/aertslab/createcisTargetdatabases>`. 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).
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).
..  Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017).
doi:10.1038/nmeth.4463_ ..  Rocklin, M. Dask: parallel computation with blocked algorithms and task scheduling. conference.scipy.org ..  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_ ..  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.
.. |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