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Peer-reviewedCC BYBioinformatics

CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data

  • Xiuwei Zhang1,2
  • Jongseok Han1,2
  • Le Song3,4,5,6
  • Xiuwei Zhang1,2
6 affiliations
  1. 1School of Computational Science and Engineering, Georgia Institute of Technology , Atlanta, GA 30332,
  2. 2Dept of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332
  3. 3GenBio AI , Palo Alto, CA, 94301,
  4. 4Mohamed bin Zayed University of Artificial Intelligence , Abu Dhabi,
  5. 5Biomap
  6. 6MBZUAI
Open at sourceDOICite

Abstract

Motivation Single-cell sequencing technologies allow researchers to study cell-cell variation within a cell population. Variations between cells are driven by the underlying biological network, particularly gene regulatory networks (GRNs). GRNs rewire as cells evolve, and different cells can have different GRNs. However, while single-cell RNA-sequencing (scRNA-seq) and single-cell multi-omics data have been used to reconstruct GRNs, the output GRNs are rarely cell-specific, but rather, most existing methods infer population-level or cell-type-level GRNs. Results We propose CeSpGRN (Cell-Specific Gene Regulatory Network inference), a method that infers cell-specific GRNs from scRNA-seq, paired scRNA-seq and scATAC-seq, or spatial transcriptomic data. In particular, existing methods that use matching scRNA-seq and scATAC-seq data incorporate population-level region information in GRN inference, whereas CeSpGRN utilizes single-cell resolution region information. CeSpGRN infers cell-specific GRNs using a kernel-weighted Gaussian Copula Graphical Model, and incorporates multi-omic or spatial location information when constructing the objective function. We tested CeSpGRN on both simulated and real datasets, and the results show that CeSpGRN has a superior performance compared to baseline methods in reconstructing GRNs and detecting regulatory interactions that differ between cells. CeSpGRN uncovered regulatory interactions that rewire during biological processes on real datasets. Availability and implementation CeSpGRN is a Python package available at https://github.com/PeterZZQ/CeSpGRN.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026cespgrn,
  title = {{CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data}},
  author = {Xiuwei Zhang and Jongseok Han and Le Song and Xiuwei Zhang},
  journal = {Bioinformatics},
  year = {2026},
  month = may,
  doi = {10.1093/bioinformatics/btag324},
  url = {https://doi.org/10.1093/bioinformatics/btag324},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data
AU  - Xiuwei Zhang
AU  - Jongseok Han
AU  - Le Song
AU  - Xiuwei Zhang
PY  - 2026
DA  - 2026/05/30
JO  - Bioinformatics
DO  - 10.1093/bioinformatics/btag324
UR  - https://doi.org/10.1093/bioinformatics/btag324
AB  - Motivation Single-cell sequencing technologies allow researchers to study cell-cell variation within a cell population. Variations between cells are driven by the underlying biological network, particularly gene regulatory networks (GRNs). GRNs rewire as cells evolve, and different cells can have different GRNs. However, while single-cell RNA-sequencing (scRNA-seq) and single-cell multi-omics data have been used to reconstruct GRNs, the output GRNs are rarely cell-specific, but rather, most existing methods infer population-level or cell-type-level GRNs. Results We propose CeSpGRN (Cell-Specific Gene Regulatory Network inference), a method that infers cell-specific GRNs from scRNA-seq, paired scRNA-seq and scATAC-seq, or spatial transcriptomic data. In particular, existing methods that use matching scRNA-seq and scATAC-seq data incorporate population-level region information in GRN inference, whereas CeSpGRN utilizes single-cell resolution region information. CeSpGRN infers cell-specific GRNs using a kernel-weighted Gaussian Copula Graphical Model, and incorporates multi-omic or spatial location information when constructing the objective function. We tested CeSpGRN on both simulated and real datasets, and the results show that CeSpGRN has a superior performance compared to baseline methods in reconstructing GRNs and detecting regulatory interactions that differ between cells. CeSpGRN uncovered regulatory interactions that rewire during biological processes on real datasets. Availability and implementation CeSpGRN is a Python package available at https://github.com/PeterZZQ/CeSpGRN.
SN  - 1367-4803
SN  - 1367-4811
ER  -