The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.
Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference
Open accessPublished version of a preprint
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 (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]
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Scenic: single-cell regulatory network inference and clustering.
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., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Generalizing RNA velocity to transient cell states through dynamical modeling
Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951
Meinshausen, N., & Bühlmann, P. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00740.x · Cited by 2,463
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012776 · Cited by 2,131
Adult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714
Wisdom of crowds for robust gene network inference
The DREAM5 Consortium, Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2016 · Cited by 1,667
The Technology and Biology of Single-Cell RNA Sequencing
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487
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Items 101–116 of 116
Principles, methods and applications of gene regulatory network inference
Jiang, Y., Liu, Z., Zhang, Q., Chen, Z., Zan, Z., Lü, Q., Wang, J., Zhu, J., Zhang, J., & Wei, X. (2025) · Scientia Sinica Vitae · doi:10.1360/ssv-2025-0070
Link prediction for biomedical network reconstruction
Cao, Z., Pan, Y., Liu, C., Zhang, Y., Guan, J., Zhou, S., & Ou, C. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.131144
Koumadorakis, D. E., Dimitrakopoulos, G. Ν., Exarchos, T. P., Vlamos, P., & Vrahatis, A. G. (2025) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213025400147
Tong, T. P., & Pang, J. (2025) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3765612.3767196
spaNetReg: A Dependency-Aware Graph Neural Network for Inferring TRNS from Spatial ATAC-Seq
Miao, J., Hou, P., & Tian, T. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356838
TPLN: Cell-Type-Specific Gene Regulatory Network Inference Based on Poisson Log-Normal Model
Huang, S., Fu, W., Yi, M., & Zhu, Y. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356563
Meng, Y., Yao, M., Han, T., Zhao, H., Liu, Z., & Ma, B. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114350 · Cited by 1
Martinelli, J. (2022) · theses.fr (ABES) · doi:10.70675/5488e904z57a3z4fd6z83d2z795ca0557b09
Danlos, F. (2021) · theses.fr (ABES) · doi:10.70675/ff8cb58bzc391z43f8zbd32zc8c4b2cdc725
Unsupervised representation learning for single-cell transcriptomic and epigenomic data
Raimundo, F. (2022) · theses.fr (ABES) · doi:10.70675/760cd5d0z17c6z4f4ezb476z68bd387daf22
Modeling Dynamics, Cell Type Specificity, and Perturbations in Gene Regulatory Networks
Shin, J., Halberg, S., Liu, Y., Hazra, S., Lee, E. D.-I., & Roy, S. (2026) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120922-103729
Tong, T. P., Wang, A., Panagopoulos, G., & Pang, J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-25311-8_21 · Cited by 2
Zhang, H., & Liu, Z. (2026) · Journal of the American Statistical Association · doi:10.1080/01621459.2026.2697050
Emma Paul, M., Jereesh, A. S., & Kumar, G. S. (2026) · Discover Computing · doi:10.1007/s10791-026-10713-w
Vishwakarma, P., Samanta, D., Basotra, N., Pal, V., Sani, R. K., & Rai, R. (2026) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-026-00562-5
Metadata
- 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
- 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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