A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data
Abstract
Motivation Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes. Results We present a novel approach for modelling and inferring gene regulatory networks from high-throughput time series and pseudo-temporally sorted single-cell data. Our method is based on a first-order autoregressive moving-average model and it infers the gene regulatory network within a variational Bayesian framework. We validate our method with synthetic data and we apply it to single cell qPCR and RNA-Seq data for mouse embryonic cells and hematopoietic cells in zebra fish. Availability and implementation The method presented in this article is available at https://github.com/mscastillo/GRNVBEM. Contact mscastillo@ugr.es.
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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 [25]
Cites
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · see citing works
Wilson, N. K., Kent, D. G., Buettner, F., Shehata, M., Macaulay, I. C., Calero-Nieto, F. J., Sánchez Castillo, M., Oedekoven, C. A., Diamanti, E., Schulte, R., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.04.004 · Cited by 466 · see citing works
Self-citationSingle-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells
Macaulay, I. C., Svensson, V., Labalette, C., Ferreira, L., Hamey, F., Voet, T., Teichmann, S. A., & Cvejic, A. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.082 · Cited by 191 · see citing works
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Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · see citing works
Mapping gene regulatory networks from single-cell omics data
Fiers, M. W. E. J., Minnoye, L., Aibar, S., Bravo González-Blas, C., Kalender Atak, Z., & Aerts, S. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx046 · Cited by 235 · see citing works
Single-cell transcriptomics unveils gene regulatory network plasticity
Iacono, G., Massoni-Badosa, R., & Heyn, H. (2019) · Genome Biology · doi:10.1186/s13059-019-1713-4 · Cited by 218 · see citing works
Temporal modelling using single-cell transcriptomics
Ding, J., Sharon, N., & Bar-Joseph, Z. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00444-7 · Cited by 195 · see citing works
Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe
Qiu, X., Rahimzamani, A., Wang, L., Ren, B., Mao, Q., Durham, T., McFaline-Figueroa, J. L., Saunders, L., Trapnell, C., & Kannan, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.02.003 · Cited by 185 · see citing works
Network inference with Granger causality ensembles on single-cell transcriptomics
Deshpande, A., Chu, L.-F., Stewart, R., & Gitter, A. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110333 · Cited by 130 · see citing works
Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data
Yuan, Q., & Duren, Z. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02182-7 · Cited by 118 · see citing works
Network modeling of single-cell omics data: challenges, opportunities, and progresses
Blencowe, M., Arneson, D., Ding, J., Chen, Y.-W., Saleem, Z., & Yang, X. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180176 · Cited by 73 · see citing works
Li, H., Sun, Y., Hong, H., Huang, X., Tao, H., Huang, Q., Wang, L., Xu, K., Gan, J., Chen, H., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00469-5 · Cited by 66 · see citing works
Bi, X.-P., 深圳518083，中国, 深., BGI-Shenzhen, S., Zhang, G.-J., 650223，中国, 中. 昆., 650223，中国, 中. 昆., Villum Center for Biodiversity Genomics, S. F. E. A. E., Center for Excellence in Animal Evolution and Genetics, C. A. O. S., State Key Laboratory of Genetic Resources and Evolution, K. I. O. Z., & Villum Center for Biodiversity Genomics, S. F. E. A. E. (2021) · Zoological Research · doi:10.24272/j.issn.2095-8137.2021.066 · Cited by 15 · see citing works
- Authors
- Sanchez-Castillo, M., Blanco, D., Tienda-Luna, I. M., Carrion, M. C., & Huang, Y.
- Affiliations
- Department of Applied Physics, University of Granada, Granada, Spain
- Universidad de Granada (ES)
- The University of Texas at San Antonio (US)
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-09-25
- Volume
- 34
- Issue
- 6
- Pages
- 964-970
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2757021802
- Cited by
- 151
- References
- 3
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 17 · 2023: 23 · 2022: 23 · 2021: 20 · 2020: 19 · 2019: 6 · 2018: 10 · 2017: 1
- FWCI
- 4.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- 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 data, time series, ARMA models, variational inference, single-cell RNA sequencing, single-cell qPCR, mouse embryonic cells
- MeSH terms
- Algorithms, Animals, Bayes Theorem, Models, Biological, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Mice, Gene Regulatory Networks, Single-Cell Analysis
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