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A scalable SCENIC workflow for single-cell gene regulatory network analysis

Nature Protocols volume 15, pages 2247–2276 (2020)Cite this article

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Abstract

This protocol explains how to perform a fast SCENIC analysis alongside standard best practices steps on single-cell RNA-sequencing data using software containers and Nextflow pipelines. SCENIC reconstructs regulons (i.e., transcription factors and their target genes) assesses the activity of these discovered regulons in individual cells and uses these cellular activity patterns to find meaningful clusters of cells. Here we present an improved version of SCENIC with several advances. SCENIC has been refactored and reimplemented in Python (pySCENIC), resulting in a tenfold increase in speed, and has been packaged into containers for ease of use. It is now also possible to use epigenomic track databases, as well as motifs, to refine regulons. In this protocol, we explain the different steps of SCENIC: the workflow starts from the count matrix depicting the gene abundances for all cells and consists of three stages. First, coexpression modules are inferred using a regression per-target approach (GRNBoost2). Next, the indirect targets are pruned from these modules using cis-regulatory motif discovery (cisTarget). Lastly, the activity of these regulons is quantified via an enrichment score for the regulon’s target genes (AUCell). Nonlinear projection methods can be used to display visual groupings of cells based on the cellular activity patterns of these regulons. The results can be exported as a loom file and visualized in the SCope web application. This protocol is illustrated on two use cases: a peripheral blood mononuclear cell data set and a panel of single-cell RNA-sequencing cancer experiments. For a data set of 10,000 genes and 50,000 cells, the pipeline runs in <2 h.

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Fig. 1: Schematic overview of the pipeline.
Fig. 2: Speed comparison of complete SCENIC workflow.
Fig. 3: Summary statistics for the unfiltered counts matrix for the PBMC study case.
Fig. 4: Summary statistics for the counts matrix after filtering for the PBMC study case.
Fig. 5: Table of enriched motifs from cisTarget for a selected set of regulons related to B cells, generated within the SCENIC workflow.
Fig. 6: AUC distribution across cells for three sample PBMC regulons.
Fig. 7: Dimensionality reduction plots for the PBMC study case.
Fig. 8: The SCope tool enables interactive comparison of multiple visualizations for the PBMC study case.
Fig. 9: The SCope tool allows exploration of regulons.
Fig. 10: Regulon specificity score for each PBMC subtype.
Fig. 11: Extended analysis of the EBF1 regulon performed in iRegulon.
Fig. 12: Overview of cancer single cell transcriptomics experiments.
Fig. 13: Binary heat map for the skin cutaneous melanoma (SKCM) data set.

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Data availability

All data analyzed within this protocol are publicly available. The PBMC 10k data set is directly available for download from the 10x Genomics company website: https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_v3. The following data sets are available from the National Center for Biotechnology Information’s GEO and are accessible through GEO Series accession numbers: GSE60361 (mouse brain data set), GSE115978 (human cutaneous melanoma), and GSE103322 (human HNSC). The non-small cell lung carcinoma data set can be downloaded from ArrayExpress (experiments E-MTAB-6149 and E-MTAB-6653). Additional metadata are available as the supplementary information files from the original publications that generated these data sets. The online version of the case studies used in this protocol is available on GitHub (https://github.com/aertslab/SCENICprotocol), including Jupyter notebooks, and the Nextflow project code, along with associated installation and usage instructions.

Code availability

SCENIC is available as a Python package at https://pypi.org/project/pyscenic/, and its source code is available on GitHub (https://github.com/aertslab/pySCENIC). The code in this manuscript has been peer reviewed.

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Acknowledgements

This work was funded by VLAIO (no. HBC.2017.1003 to J.R., Y.S., and S. Aerts); by an ERC Consolidator Grant (no. 724226_cis-CONTROL to S. Aerts); and by the KU Leuven (grant no. C14/18/092 to S. Aerts). Computing was performed at the Vlaams Supercomputer Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Bram Van de Sande, Christopher Flerin.

Authors and Affiliations

  1. VIB Center for Brain & Disease Research, KU Leuven, Leuven, Belgium

    Bram Van de Sande, Christopher Flerin, Kristofer Davie, Maxime De Waegeneer, Gert Hulselmans, Sara Aibar & Stein Aerts

  2. Department of Human Genetics, KU Leuven, Leuven, Belgium

    Bram Van de Sande, Christopher Flerin, Maxime De Waegeneer, Gert Hulselmans, Sara Aibar & Stein Aerts

  3. Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium

    Ruth Seurinck, Wouter Saelens, Robrecht Cannoodt, Quentin Rouchon & Yvan Saeys

  4. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Ruth Seurinck, Wouter Saelens, Robrecht Cannoodt, Quentin Rouchon & Yvan Saeys

  5. Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium

    Robrecht Cannoodt

  6. Janssen Pharmaceutica, Beerse, Belgium

    Toni Verbeiren, Dries De Maeyer & Joke Reumers

  7. Data Intuitive, Ghent, Belgium

    Toni Verbeiren

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  1. Bram Van de Sande
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Contributions

Conceptualization: B.V.d.S., C.F., J.R., Y.S., and S. Aerts; methodology: B.V.d.S., C.F., K.D., M.D.W., G.H., S. Aibar, R.S., W.S., R.C., Q.R., T.V., D.D.M., J.R., Y.S., and S. Aerts; software: B.V.d.S., C.F., K.D., M.D.W., G.H., S. Aibar, R.S., W.S., R.C., Q.R., T.V., and D.D.M.; validation, resources, and data curation: B.V.d.S. and C.F.; writing—original draft: B.V.d.S., C.F., and S. Aerts; writing—review and editing: B.V.d.S., C.F., and S. Aerts; visualization: B.V.d.S., C.F., and S. Aerts; supervision: S. Aerts., Y.S., and J.R.

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Correspondence to Stein Aerts.

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Key references using this protocol

Davie, K. et al. Cell 174, 982–998 (2018): https://doi.org/10.1016/j.cell.2018.05.057

Lambrechts, D. et al. Nat. Med. 24, 1277–1289 (2018): https://doi.org/10.1038/s41591-018-0096-5

Wouters, J. et al. Preprint at bioRxiv (2019): https://www.biorxiv.org/content/10.1101/715995v2

Aibar, S. et al. Nat. Methods 14, 1083–1086 (2017): https://doi.org/10.1038/nmeth.4463

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Van de Sande, B., Flerin, C., Davie, K. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc 15, 2247–2276 (2020). https://doi.org/10.1038/s41596-020-0336-2

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