{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:48:58Z","timestamp":1774615738771,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100007225","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["MOST 109-2221-E-002-161-MY3"],"award-info":[{"award-number":["MOST 109-2221-E-002-161-MY3"]}],"id":[{"id":"10.13039\/100007225","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007225","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["109-2320-B-002-017-MY3"],"award-info":[{"award-number":["109-2320-B-002-017-MY3"]}],"id":[{"id":"10.13039\/100007225","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007225","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["109-2221-E-010-012-MY3"],"award-info":[{"award-number":["109-2221-E-010-012-MY3"]}],"id":[{"id":"10.13039\/100007225","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level.<\/jats:p>","DOI":"10.1093\/bib\/bbac633","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T04:54:07Z","timestamp":1674104047000},"source":"Crossref","is-referenced-by-count":1,"title":["Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts"],"prefix":"10.1093","volume":"24","author":[{"given":"Yueh-Hua","family":"Tu","sequence":"first","affiliation":[{"name":"Academia Sinica Bioinformatics Program, Taiwan International Graduate Program, , Taipei, 115 , Taiwan"},{"name":"National Taiwan University Taiwan International Graduate Program on Bioinformatics, , Taipei, 106 , Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4876-3309","authenticated-orcid":false,"given":"Hsueh-Fen","family":"Juan","sequence":"additional","affiliation":[{"name":"National Taiwan University Taiwan International Graduate Program on Bioinformatics, , Taipei, 106 , Taiwan"},{"name":"National Taiwan University Department of Life Science, , Taipei, 106 , Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3386-0934","authenticated-orcid":false,"given":"Hsuan-Cheng","family":"Huang","sequence":"additional","affiliation":[{"name":"National Yang Ming Chiao Tung University Institute of Biomedical Informatics, , Taipei, 112 , Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"2023032004261632200_","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","article-title":"Scanpy: large-scale single-cell gene expression data analysis","volume":"19","author":"Alexander Wolf","year":"2018","journal-title":"Genome Biol"},{"key":"2023032004261632200_","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"Satija","year":"2015","journal-title":"Nat Biotechnol"},{"key":"2023032004261632200_","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat 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