Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference
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Abstract
Motivation: Single-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulatory network (GRNs) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology. Results: In this work, we propose a new method named GRISLI for de novo GRN inference from scRNA-seq data. GRISLI infers a velocity vector field in the space of scRNA-seq data from profiles of individual cells, and models the dynamics of cell trajectories with a linear ordinary differential equation to reconstruct the underlying GRN with a sparse regression procedure. We show on real data that GRISLI outperforms a recently proposed state-of-the-art method for GRN reconstruction from scRNA-seq data. Availability and implementation: The MATLAB code of GRISLI is available at: https://github.com/PCAubin/GRISLI. Supplementary information: Supplementary data are available at Bioinformatics online.
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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 [27]
The Abstract says scRNA-seq offers possibilities for inferring gene regulatory networks, supporting the general inference claim. It describes the paper’s approach as velocity inference and differential-equation modeling, but does not support the more specific claim of cell type-specific inference through co-expression analysis such as PIDC or SCENIC. (LLM, conf=0.85)
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Kolmogorov-Arnold Network for Gene Regulatory Network Inference
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- Authors
- Aubin-Frankowski, P.-C., & Vert, J.-P.
- Affiliations
- Université Paris Sciences et Lettres (FR)
- Google (United States) (US)
- Corresponding author
- Aubin-Frankowski, Pierre-Cyril
- Vert, Jean-Philippe
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-06-17
- Volume
- 36
- Issue
- 18
- Pages
- 4774-4780
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W3036077871
- Cited by
- 116
- References
- 38
- Citations by year
- 2026: 9 · 2025: 18 · 2024: 20 · 2023: 23 · 2022: 23 · 2021: 17 · 2020: 4 · 2019: 2
- FWCI
- 5.44
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- gene regulatory network inference, single-cell RNA sequencing, linear differential equations, sparse regression, cell differentiation, cell cycle
- MeSH terms
- RNA-Seq, Sequence Analysis, RNA, Gene Expression Profiling, Gene Regulatory Networks, Single-Cell Analysis
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