LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering
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Summary: To construct gene co-expression networks based on single-cell RNA-Sequencing data, we present an algorithm called LEAP, which utilizes the estimated pseudotime of the cells to find gene co-expression that involves time delay. Availability and Implementation: R package LEAP available on CRAN 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 [28]
The Methods and Conclusion describe using scRNA-seq co-expression to identify directional associations that may reflect gene regulation. The Results apply LEAP to dendritic cells, but the paper does not establish cell type-specific trans-regulation inference; it characterizes the inferred associations as likely regulatory rather than confirmed regulation. (LLM, conf=0.84)
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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
Comparing Statistical Methods for Constructing Large Scale Gene Networks
Allen, J. D., Xie, Y., Chen, M., Girard, L., & Xiao, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029348 · Cited by 186 · see citing works
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Research works printed but not linked: 3
- Campbell,K. et al. (2015) Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles. bioRxiv, 027219.
- Campbell,K., and Yau,C. (2015) Bayesian Gaussian Process Latent Variable Models for pseudotime inference in single-cell RNA-seq data. bioRxiv, 026872.
- Reid,J.E., and Wernisch,L. (2015) Pseudotime estimation: deconfounding sin- gle cell time series. bioRxiv, 019588.
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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
Chen, S., & Mar, J. C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2217-z · 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
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
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
Sebastian, S., Roy, S., & Kalita, J. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104797 · Cited by 10 · see citing works
Zinati, Y., Takiddeen, A., & Emad, A. (2023) · doi:10.1101/2023.07.25.550225 · Cited by 36 · see citing works
- Authors
- Specht, A. T., & Li, J.
- Affiliations
- University of Notre Dame (US)
- Corresponding author
- Li, Jun
- ORCID
- Li, Jun
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-12-09
- Volume
- 33
- Issue
- 5
- Pages
- 764-766
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC5860270
- OpenAlex
- W2561884319
- Cited by
- 193
- References
- 2
- Citations by year
- 2026: 17 · 2025: 33 · 2024: 32 · 2023: 29 · 2022: 25 · 2021: 21 · 2020: 18 · 2019: 8 · 2018: 6 · 2017: 4
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Indiana Clinical and Translational Sciences Institute (UL1TR001108)
- National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award
- 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 › Bioinformatics and Genomic Networks
- Keywords
- gene co-expression network, single-cell RNA sequencing, cell trajectory inference, R package
- MeSH terms
- Algorithms, Animals, Dendritic Cells, RNA, Software, Gene Expression, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Mice, Gene Regulatory Networks, Single-Cell Analysis
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