{"title":"scSAGRN:利用空间关联推断单细胞多组学的基因调控网络","authors":"Qing Ren, Mengdi Nan, Yuhan Fu, Xiang Chen, Yibing Ma, Yongle Shi, Jie Gao","doi":"10.1016/j.biosystems.2025.105531","DOIUrl":null,"url":null,"abstract":"<div><div>Identifying the regulatory relationships between transcription factors and target genes is fundamental to understanding molecular regulatory mechanisms in biological processes including development and disease occurrence. Therefore, resolving the relationships between cis-regulatory elements and genes using single-cell multi-omics data is important for understanding transcriptional regulation. Here, scSAGRN is proposed as a framework for inferring gene regulatory networks from single-cell multi-omics. scSAGRN incorporates spatial association to compute correlations between gene expression and chromatin openness data, connects distal cis-regulatory elements to genes, infers gene regulatory networks and identifies key transcription factors. The approach is benchmarked using real single-cell datasets, and scSAGRN shows superior performance in TF recovery, peak-gene linkage prediction, and TF-gene linkage prediction compared to existing methods. Meanwhile, in human peripheral blood mononuclear cells dataset, mouse cerebral cortex dataset and mouse embryonic brain cells dataset, scSAGRN demonstrates its capability to infer gene regulatory networks and identify transcription factors. Overall, scSAGRN provides a reference for predicting transcriptional regulatory patterns from single-cell multi-omics data.</div></div>","PeriodicalId":50730,"journal":{"name":"Biosystems","volume":"254 ","pages":"Article 105531"},"PeriodicalIF":1.9000,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"scSAGRN: Inferring gene regulatory networks from single-cell multi-omics using spatial association\",\"authors\":\"Qing Ren, Mengdi Nan, Yuhan Fu, Xiang Chen, Yibing Ma, Yongle Shi, Jie Gao\",\"doi\":\"10.1016/j.biosystems.2025.105531\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Identifying the regulatory relationships between transcription factors and target genes is fundamental to understanding molecular regulatory mechanisms in biological processes including development and disease occurrence. Therefore, resolving the relationships between cis-regulatory elements and genes using single-cell multi-omics data is important for understanding transcriptional regulation. Here, scSAGRN is proposed as a framework for inferring gene regulatory networks from single-cell multi-omics. scSAGRN incorporates spatial association to compute correlations between gene expression and chromatin openness data, connects distal cis-regulatory elements to genes, infers gene regulatory networks and identifies key transcription factors. The approach is benchmarked using real single-cell datasets, and scSAGRN shows superior performance in TF recovery, peak-gene linkage prediction, and TF-gene linkage prediction compared to existing methods. Meanwhile, in human peripheral blood mononuclear cells dataset, mouse cerebral cortex dataset and mouse embryonic brain cells dataset, scSAGRN demonstrates its capability to infer gene regulatory networks and identify transcription factors. Overall, scSAGRN provides a reference for predicting transcriptional regulatory patterns from single-cell multi-omics data.</div></div>\",\"PeriodicalId\":50730,\"journal\":{\"name\":\"Biosystems\",\"volume\":\"254 \",\"pages\":\"Article 105531\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2025-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biosystems\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0303264725001418\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0303264725001418","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
scSAGRN: Inferring gene regulatory networks from single-cell multi-omics using spatial association
Identifying the regulatory relationships between transcription factors and target genes is fundamental to understanding molecular regulatory mechanisms in biological processes including development and disease occurrence. Therefore, resolving the relationships between cis-regulatory elements and genes using single-cell multi-omics data is important for understanding transcriptional regulation. Here, scSAGRN is proposed as a framework for inferring gene regulatory networks from single-cell multi-omics. scSAGRN incorporates spatial association to compute correlations between gene expression and chromatin openness data, connects distal cis-regulatory elements to genes, infers gene regulatory networks and identifies key transcription factors. The approach is benchmarked using real single-cell datasets, and scSAGRN shows superior performance in TF recovery, peak-gene linkage prediction, and TF-gene linkage prediction compared to existing methods. Meanwhile, in human peripheral blood mononuclear cells dataset, mouse cerebral cortex dataset and mouse embryonic brain cells dataset, scSAGRN demonstrates its capability to infer gene regulatory networks and identify transcription factors. Overall, scSAGRN provides a reference for predicting transcriptional regulatory patterns from single-cell multi-omics data.
期刊介绍:
BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.