pySCENIC

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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.

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 [5] (see here).

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 is available on Read the Docs

News and releases

0.10.2 | 2020-06-05

  • Bugfix for CLI grn step

0.10.1 | 2020-05-17

  • CLI: file compression (optionally) enabled for intermediate files for the major steps: grn (adjacencies matrix), ctx (regulons), and aucell (auc matrix). Compression is used when the file name argument has a .gz ending.

0.10.0 | 2020-02-27

  • Added a helper script arboreto_with_multiprocessing.py that runs the Arboreto GRN algorithms (GRNBoost2, GENIE3) without Dask for compatibility.
  • Ability to set a fixed seed in both the AUCell step and in the calculation of regulon thresholds (CLI parameter --seed; aucell function parameter seed).
  • (since 0.9.18) In the modules_from_adjacencies function, the default value of rho_mask_dropouts is changed to False. This now matches the behavior of the R version of SCENIC. The cli version has an additional option to turn dropout masking back on (--mask_dropouts).

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, or SCENIC (R version). 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).
[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).
[4]Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).
[5]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