Scenic: single-cell regulatory network inference and clustering.
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Abstract
We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
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Lineage-committed TF-TG networks were identified based on correlation between enrichment of TGs in cells and cell pseudotime of the main lineage (correlation > 0.3 or < -0.3).
“We identified lineage-committed TF–TG networks on the basis of the correlation between enrichment of TGs in cells (measured by AUC of recovery curve of the TGs) and cell pseudotime of the main lineage inferred by Slingshot (correlation > 0.3 or < -0.3; Figure 5A).”
From Single-Cell Transcriptome Analysis Reveals Six Subpopulations Reflecting Distinct Cellular Fates in Senescent Mouse Embryonic Fibroblasts (2020) · cites this paper as Aibar et al., 2017
The Online Methods (AUCell; page unavailable) support measuring target-gene-set enrichment in each cell as the area under a recovery curve. The cited paper does not describe identifying lineage-committed networks by correlating those scores with Slingshot pseudotime, or using the stated ±0.3 cutoff. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSCENIC nominated AP1 and ETS family members as TFs potentially controlling TSKs.
“Therefore, we performed single-cell regulatory network inference and clustering (SCENIC) (Aibar et al., 2017), which nominated AP1 and ETS family members as TFs potentially controlling TSKs”
From Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma (2020) · cites this paper as Aibar et al., 2017
hdWGCNA is a purely unsupervised approach and does not require prior knowledge or databases in the inference procedure.
“Unlike other network analysis pipelines such as single-cell regulatory network inference and clustering (SCENIC)70 or CellChat,71 hdWGCNA is a purely unsupervised approach and does not require prior knowledge or databases in the inference procedure.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [70]
Single-cell RNA sequencing data enables cell type-specific trans-regulation inference through co-expression analysis.
“Single-cell RNA sequencing (scRNA-seq) data enables cell type-specific trans-regulation inference through co-expression analysis such as PIDC and SCENIC21–30.”
From Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data (2025) · cites this paper as [22]
The highest regulon activity in XO4+ cells was identified for Hif1a and Elf3 TFs.
“The highest regulon’s activity in XO4+ cells was identified for Hif1a and Elf3 TFs”
From Transcriptional signature in microglia associated with Aβ plaque phagocytosis (2021) · cites this paper as [49]
The paper describes scoring regulon activity in individual cells (SCENIC workflow, page unavailable), but the passages found do not report results for XO4+ cells or identify Hif1a and Elf3 as their most active regulons. Searches for “XO4+ cells Hif1a Elf3 regulon activity” and “Hif1a Elf3 transcription factors XO4” found no passage addressing that result. (LLM, conf=0.74)
Experimental — accuracy not yet measured
Gene-regulatory network modelling approaches reconstruct systematic gene-gene associations from unperturbed single-cell omics data.
“In this respect, gene-regulatory network (GRN) modelling approaches are promising as they reconstruct systematic gene–gene associations from unperturbed single-cell omics data6–11.”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [7]
CellOracle uses a custom GRN modelling method that incorporates promoter and TF-binding information with scRNA-seq data to infer a directional GRN.
“Thus, we developed a custom GRN modelling method motivated by previous approaches that incorporate promoter and TF-binding information with scRNA-seq data to infer a directional GRN7 (Extended Data Fig. 1a and Methods).”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [7]
Because of the linear regression constraint, we cannot use pre-existing GRN inference algorithms, such as GENIE3 and GRNboost.
“Because of this second constraint, we cannot use pre-existing GRN inference algorithms, such as GENIE3 and GRNboost (refs. 7,51).”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [7]
The SCENIC algorithm integrates a tree-based GRN inference algorithm with information on TF binding.
“The SCENIC algorithm integrates a tree-based GRN inference algorithm with information on TF binding7.”
From Dissecting cell identity via network inference and in silico gene perturbation (2023) · cites this paper as [7]
SCENIC has shown similar robustness to batch effect.
“We further note that SCENIC19 has shown similar robustness to batch effect and that this is likely a general characteristic of GRN-based dimensionality reduction approaches that is not specific to SCING.”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [19]
SCENIC prunes edges based on known transcription factor binding sites.
“SCENIC,19 an extension of GRNBOOST2, prunes edges based on known transcription factor binding sites (TFBS).”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [19]
SCENIC infers a GRN directly from the data, identifying direct regulatory interactions between transcription factors and targets by integrating co-expression with motif enrichment analysis.
“Finally, SCENIC [54]—developed in our laboratory—infers a GRN directly from the data, identifying direct regulatory interactions between TFs and targets by integrating co-expression with motif enrichment analysis.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [54]
The activity of the SCENIC-inferred GRN in each cell is used to identify cell types/states and key transcription factors that characterize each state with higher accuracy.
“The activity of this GRN in each cell is then used to identify cell types/states, and key TFs that characterize each state with higher accuracy.”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [54]
The SCENIC package utilises cis-regulatory information to reinforce transcriptional gene network learning.
“For instance, the SCENIC package utilises cis‐regulatory information to reinforce transcriptional gene network learning (Aibar et al, 2017).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Aibar et al, 2017
Data can be extracted from scRNA-seq datasets using SCENIC, but the lack of spatial information is likely to confound important associations.
“These data can be extracted from scRNA-seq datasets using algorithms such as single-cell regulatory network inference and clustering (SCENIC) (Aibar et al., 2017), but the lack of spatial information is likely to confound important associations.”
From Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays (2022) · cites this paper as Aibar et al., 2017
These genes were in a single inhibitory program (Inh-22, from NMF) that included the SCENIC-predicted subtype regulators FOXP2 and LEF1.
“These genes were in a single inhibitory program (Inh-22, from NMF) that included the SCENIC-predicted subtype regulators FOXP2 and LEF134,36”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [36]
Excitatory neurons in the thalamus were predominantly composed (74%) of a subtype (NXPH1+RNF220+) not observed in the neocortex and predicted to be regulated by LHX9, SOX2, SHOX2 and TCF7L2.
“Excitatory neurons in the TH were predominantly composed (74%) of a subtype (NXPH1+RNF220+) that was not observed in the neocortex and is predicted to be regulated by LHX9, SOX2, SHOX2 and TCF7L234,36”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [36]
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- Authors
- Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Atak, Z. K., Wouters, J., & Aerts, S.
- Affiliations
- VIB-KU Leuven Center for Brain & Disease Research (BE)
- IMEC (BE)
- VIB-KU Leuven Center for Cancer Biology (BE)
- University of Liège (BE)
- Corresponding author
- Aerts, Stein
- ORCID
- Venue
- Nature Methods
- Published
- 2017-10-09
- Volume
- 14
- Issue
- 11
- Pages
- 1083-1086
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC5937676
- OpenAlex
- W2616922646
- Semantic Scholar
- 469203c63af18c9c25789e4aa127132c2caa99e4
- Cited by
- 6,520
- References
- 100
- Citations by year
- 2026: 1222 · 2025: 1291 · 2024: 1268 · 2023: 1012 · 2022: 729 · 2021: 494 · 2020: 302 · 2019: 141 · 2018: 54 · 2017: 4
- FWCI
- 57.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Lirias (submittedVersion, other-oa)
- ORBi (University of Liège) (submittedVersion, other-oa)
- Document Server@UHasselt (UHasselt) (submittedVersion)
- Lirias (KU Leuven) (submittedVersion)
- Funders
- Fonds Wetenschappelijk Onderzoek (G.0791.14, G.0640.13)
- KU Leuven (PF/10/016, OT/13/103)
- Kom op tegen Kanker
- Vlaamse regering
- Fondation contre le Cancer (2012-F2)
- Bijzonder Onderzoeksfonds UGent (PF/10/016)
- 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
- gene regulatory network inference, cell clustering, single-cell RNA sequencing, cis-regulatory elements, transcription factors, cell states, cellular heterogeneity, SCENIC
- MeSH terms
- Algorithms, Animals, Brain, Humans, Cluster Analysis, Gene Expression Profiling, Mice, Gene Regulatory Networks, Single-Cell Analysis
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