A scalable SCENIC workflow for single-cell gene regulatory network analysis
Open accessAbstract
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…
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Hif1a was also identified as a driving TF in XO4+ microglia using pySCENIC.
“Hif1a was also identified as a driving TF in XO4+ microglia using pySCENIC50.”
From Transcriptional signature in microglia associated with Aβ plaque phagocytosis (2021) · cites this paper as [50]
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References 64 · Cited by 1,494
- Authors
- Van de Sande, B., Flerin, C., Davie, K., De Waegeneer, M., Hulselmans, G., Aibar, S., Seurinck, R., Saelens, W., Cannoodt, R., Rouchon, Q., Verbeiren, T., De Maeyer, D., Reumers, J., Saeys, Y., & Aerts, S.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- Ghent University (BE)
- Ghent University Hospital (BE)
- Janssen (Belgium) (BE)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Nature Protocols
- Published
- 2020-06-19
- Volume
- 15
- Issue
- 7
- Pages
- 2247-2276
- ISSN
- 1750-2799 (ISSN-L)
- 1754-2189
- OpenAlex
- W3036206386
- Cited by
- 1,494
- References
- 64
- Citations by year
- 2026: 287 · 2025: 352 · 2024: 383 · 2023: 254 · 2022: 130 · 2021: 72 · 2020: 14 · 2012: 1
- FWCI
- 39.72
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- Nature Protocols (publishedVersion)
- PubMed (publishedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- SCENIC, transcription factor target genes, AUCell, cell clustering, software containers, Nextflow pipeline, peripheral blood mononuclear cells
- MeSH terms
- Animals, Humans, Cell Line, Tumor, Mice, Gene Regulatory Networks, Workflow, Single-Cell Analysis
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