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https://github.com/aertslab/pySCENIC

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.
https://github.com/aertslab/pySCENIC

gene-regulatory-network single-cell transcription-factors transcriptomics

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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.

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README 

          
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 `_



----



pySCENIC is part of the SCENIC Suite of tools! 

See the main `SCENIC website `_ for additional information and a full list of tools available.



----



News and releases

-----------------



0.12.1 | 2022-11-21

^^^^^^^^^^^^^^^^^^^



* Add support for running arboreto_with_multiprocessing.py with spawn instead of fork as multiprocessing method.Pool

* Use ravel instead of flatten to avoid unnecessary memory copy in aucell

* Update Docker image file and add separated Docker file for pySCENIC with scanpy.



0.12.0 | 2022-08-16

^^^^^^^^^^^^^^^^^^^



* Only databases in Feather v2 format are supported now (`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 `_. 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 `_.



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 `_,

or `SCENIC (R version) `_.

There is a tutorial to `create new 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 `_

.. [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 `_

.. [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 `_



.. |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