{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T03:18:40Z","timestamp":1785899920102,"version":"3.56.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["DBI-2019771"],"award-info":[{"award-number":["DBI-2019771"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM143070"],"award-info":[{"award-number":["R35GM143070"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>CeSpGRN is a Python package available at https:\/\/github.com\/PeterZZQ\/CeSpGRN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag324","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T11:41:49Z","timestamp":1780141309000},"source":"Crossref","is-referenced-by-count":1,"title":["CeSpGRN: inferring cell-specific gene regulatory networks from single-cell multi-omics and spatial data"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8198-0260","authenticated-orcid":false,"given":"Ziqi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computational Science and Engineering, Georgia Institute of Technology , Atlanta, GA 30332,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongseok","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computational Science and Engineering, Georgia Institute of Technology , Atlanta, GA 30332,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Song","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence , Abu Dhabi,","place":["United Arab Emirates"]},{"name":"GenBio AI , Palo Alto, CA, 94301,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1713-772X","authenticated-orcid":false,"given":"Xiuwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computational Science and Engineering, Georgia Institute of Technology , Atlanta, GA 30332,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"2026062409130670100_btag324-B1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1242\/dev.085290","article-title":"Otx2 is an intrinsic determinant of the embryonic stem cell state and is required for transition to a stable epiblast stem cell condition","volume":"140","author":"Acampora","year":"2013","journal-title":"Development"},{"key":"2026062409130670100_btag324-B2","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1016\/j.stemcr.2017.09.019","article-title":"Functional antagonism between otx2 and nanog specifies a spectrum of heterogeneous identities in embryonic stem cells","volume":"9","author":"Acampora","year":"2017","journal-title":"Stem Cell Rep"},{"key":"2026062409130670100_btag324-B3","doi-asserted-by":"crossref","first-page":"11878","DOI":"10.1073\/pnas.0901910106","article-title":"Recovering time-varying networks of dependencies in social and biological studies","volume":"106","author":"Ahmed","year":"2009","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026062409130670100_btag324-B4","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat Methods"},{"key":"2026062409130670100_btag324-B5","doi-asserted-by":"publisher","DOI":"10.18129\/B9.bioc.topGO","article-title":"topgo: Enrichment analysis for gene ontology","author":"Alexa","year":"2021","journal-title":"R Package Version 2.44.0"},{"key":"2026062409130670100_btag324-B6","author":"Argelaguet","year":"2022"},{"key":"2026062409130670100_btag324-B7","first-page":"485","article-title":"Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data","volume":"9","author":"Banerjee","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2026062409130670100_btag324-B8","first-page":"1","volume-title":"Found Trends Mach Learn","author":"Boyd"},{"key":"2026062409130670100_btag324-B9","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1038\/s41592-023-01938-4","article-title":"Scenic+: single-cell multiomic inference of enhancers and gene regulatory networks","volume":"20","author":"Bravo Gonz\u00e1lez-Blas","year":"2023","journal-title":"Nat Methods"},{"key":"2026062409130670100_btag324-B10","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cels.2017.08.014","article-title":"Gene regulatory network inference from single-cell data using multivariate information measures","volume":"5","author":"Chan","year":"2017","journal-title":"Cell Syst"},{"key":"2026062409130670100_btag324-B11","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1186\/s12859-018-2217-z","article-title":"Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data","volume":"19","author":"Chen","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2026062409130670100_btag324-B12","doi-asserted-by":"crossref","first-page":"e62","DOI":"10.1093\/nar\/gkz172","article-title":"Cell-specific network constructed by single-cell RNA sequencing data","volume":"47","author":"Dai","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2026062409130670100_btag324-B13","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.jmva.2004.02.009","article-title":"Sparse graphical models for exploring gene expression data","volume":"90","author":"Dobra","year":"2004","journal-title":"J Multivar Anal"},{"key":"2026062409130670100_btag324-B14","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1101\/gad.3.10.1518","article-title":"Spatial regulation of zerkn\u00fcllt: a dorsal-ventral patterning gene in drosophila","volume":"3","author":"Doyle","year":"1989","journal-title":"Genes Dev"},{"key":"2026062409130670100_btag324-B15","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1093\/biostatistics\/kxm045","article-title":"Sparse inverse covariance estimation with the graphical lasso","volume":"9","author":"Friedman","year":"2008","journal-title":"Biostatistics"},{"key":"2026062409130670100_btag324-B16","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1038\/nmeth.3971","article-title":"Diffusion pseudotime robustly reconstructs lineage branching","volume":"13","author":"Haghverdi","year":"2016","journal-title":"Nat Methods"},{"key":"2026062409130670100_btag324-B17","doi-asserted-by":"crossref","first-page":"e12776","DOI":"10.1371\/journal.pone.0012776","article-title":"Inferring regulatory networks from expression data using tree-based methods","volume":"5","author":"Huynh-Thu","year":"2010","journal-title":"PLoS One"},{"key":"2026062409130670100_btag324-B18","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1038\/s41586-022-05688-9","article-title":"Dissecting cell identity via network inference and in silico gene perturbation","volume":"614","author":"Kamimoto","year":"2023","journal-title":"Nature"},{"key":"2026062409130670100_btag324-B19","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1126\/science.aan3235","article-title":"The drosophila embryo at single-cell transcriptome resolution","volume":"358","author":"Karaiskos","year":"2017","journal-title":"Science"},{"key":"2026062409130670100_btag324-B20","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1093\/nar\/gkz1179","article-title":"Transcriptional network dynamics during the progression of pluripotency revealed by integrative statistical learning","volume":"48","author":"Kim","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2026062409130670100_btag324-B21","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"key":"2026062409130670100_btag324-B22","doi-asserted-by":"crossref","first-page":"R118","DOI":"10.1186\/gb-2013-14-10-r118","article-title":"Temporal dynamics and transcriptional control using single-cell gene expression analysis","volume":"14","author":"Kouno","year":"2013","journal-title":"Genome Biol"},{"key":"2026062409130670100_btag324-B23","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1038\/s41592-025-02651-0","article-title":"scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions","volume":"22","author":"Li","year":"2025","journal-title":"Nat Methods"},{"key":"2026062409130670100_btag324-B24","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.gpb.2020.05.005","article-title":"c-csn: single-cell RNA sequencing data analysis by conditional cell-specific network","volume":"19","author":"Li","year":"2021","journal-title":"Genom Proteom Bioinform"},{"key":"2026062409130670100_btag324-B25","doi-asserted-by":"crossref","first-page":"4471","DOI":"10.1242\/dev.125.22.4471","article-title":"Eve and ftz regulate a wide array of genes in blastoderm embryos: the selector homeoproteins directly or indirectly regulate most genes in drosophila","volume":"125","author":"Liang","year":"1998","journal-title":"Development"},{"key":"2026062409130670100_btag324-B26","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1214\/12-AOS1037","article-title":"High-dimensional semiparametric Gaussian copula graphical models","volume":"40","author":"Liu","year":"2012","journal-title":"Ann Statist"},{"key":"2026062409130670100_btag324-B27","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1038\/ng1760","article-title":"The oct4 and nanog transcription network regulates pluripotency in mouse embryonic stem cells","volume":"38","author":"Loh","year":"2006","journal-title":"Nat Genet"},{"key":"2026062409130670100_btag324-B28","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1038\/nature02782","article-title":"Genomic analysis of regulatory network dynamics reveals large topological changes","volume":"431","author":"Luscombe","year":"2004","journal-title":"Nature"},{"key":"2026062409130670100_btag324-B29","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1016\/j.cels.2022.09.002","article-title":"Belayer: modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics","volume":"13","author":"Ma","year":"2022","journal-title":"Cell Syst"},{"key":"2026062409130670100_btag324-B30","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1093\/bioinformatics\/btx194","article-title":"SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation","volume":"33","author":"Matsumoto","year":"2017","journal-title":"Bioinformatics"},{"key":"2026062409130670100_btag324-B31","doi-asserted-by":"crossref","first-page":"1482","DOI":"10.1038\/s41587-019-0336-3","article-title":"Visualizing structure and transitions in high-dimensional biological data","volume":"37","author":"Moon","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2026062409130670100_btag324-B32","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1242\/dev.000836","article-title":"The negative regulation of Mesp2 by mouse Ripply2 is required to establish the rostro-caudal patterning within a somite","volume":"134","author":"Morimoto","year":"2007","journal-title":"Development"},{"key":"2026062409130670100_btag324-B33","first-page":"343","author":"Nishihara","year":"2015"},{"key":"2026062409130670100_btag324-B34","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1093\/bioinformatics\/btx575","article-title":"SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles","volume":"34","author":"Papili Gao","year":"2018","journal-title":"Bioinformatics"},{"key":"2026062409130670100_btag324-B35","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41592-019-0690-6","article-title":"Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data","volume":"17","author":"Pratapa","year":"2020","journal-title":"Nat Methods"},{"key":"2026062409130670100_btag324-B36","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1093\/bib\/bbp057","article-title":"Toward the dynamic interactome: it\u2019s about time","volume":"11","author":"Przytycka","year":"2010","journal-title":"Brief Bioinform"},{"key":"2026062409130670100_btag324-B37","doi-asserted-by":"crossref","first-page":"607516","DOI":"10.3389\/fcell.2020.607516","article-title":"Neuromesodermal progenitors: a basis for robust axial patterning in development and evolution","volume":"8","author":"Sambasivan","year":"2021","journal-title":"Front Cell Dev Biol"},{"key":"2026062409130670100_btag324-B38","doi-asserted-by":"crossref","first-page":"630067","DOI":"10.3389\/fcell.2021.630067","article-title":"An esrrb and nanog cell fate regulatory module controlled by feed forward loop interactions","volume":"9","author":"Sevilla","year":"2021","journal-title":"Front Cell Dev Biol"},{"key":"2026062409130670100_btag324-B39","doi-asserted-by":"crossref","first-page":"i128","DOI":"10.1093\/bioinformatics\/btp192","article-title":"KELLER: estimating time-varying interactions between genes","volume":"25","author":"Song","year":"2009","journal-title":"Bioinformatics"},{"key":"2026062409130670100_btag324-B40","doi-asserted-by":"crossref","first-page":"2319","DOI":"10.1126\/science.290.5500.2319","article-title":"A global geometric framework for nonlinear dimensionality reduction","volume":"290","author":"Tenenbaum","year":"2000","journal-title":"Science"},{"key":"2026062409130670100_btag324-B41","first-page":"978","author":"Wang","year":"2014"},{"key":"2026062409130670100_btag324-B42","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1038\/s41592-023-01971-3","article-title":"Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics","volume":"20","author":"Wang","year":"2023","journal-title":"Nat Methods"},{"key":"2026062409130670100_btag324-B43","doi-asserted-by":"crossref","first-page":"e2113178118","DOI":"10.1073\/pnas.2113178118","article-title":"Constructing local cell-specific networks from single-cell data","volume":"118","author":"Wang","year":"2021","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026062409130670100_btag324-B44","doi-asserted-by":"crossref","first-page":"bbad180","DOI":"10.1093\/bib\/bbad180","article-title":"P-csn: single-cell rna sequencing data analysis by partial cell-specific network","volume":"24","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2026062409130670100_btag324-B45","doi-asserted-by":"crossref","first-page":"gkaf138","DOI":"10.1093\/nar\/gkaf138","article-title":"Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data","volume":"53","author":"Xu","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2026062409130670100_btag324-B46","doi-asserted-by":"crossref","first-page":"3064","DOI":"10.1038\/s41467-023-38637-9","article-title":"Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets","volume":"14","author":"Zhang","year":"2023","journal-title":"Nat Commun"},{"key":"2026062409130670100_btag324-B47","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1186\/s13059-022-02706-x","article-title":"Scdart: integrating unmatched scrna-seq and scatac-seq data and learning cross-modality relationship simultaneously","volume":"23","author":"Zhang","year":"2022","journal-title":"Genome Biol"},{"key":"2026062409130670100_btag324-B48","doi-asserted-by":"crossref","first-page":"16438","DOI":"10.1073\/pnas.0701014104","article-title":"A gene regulatory network in mouse embryonic stem cells","volume":"104","author":"Zhou","year":"2007","journal-title":"Proc Natl Acad Sci USA"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag324\/68445965\/btag324.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/6\/btag324\/68445965\/btag324.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/6\/btag324\/68445965\/btag324.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T13:13:16Z","timestamp":1782306796000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag324\/8699592"}},"subtitle":[],"editor":[{"given":"Laura","family":"Cantini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":48,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag324","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,6]]},"published":{"date-parts":[[2026,6,1]]},"article-number":"btag324"}}