pyscenic

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