Jiang Junyao, Lyu Pin, Li Jinlian, Huang Sunan, Tao Jiawang, Blackshaw Seth, Qian Jiang, Wang Jie
CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
iScience. 2022 Oct 14;25(11):105359. doi: 10.1016/j.isci.2022.105359. eCollection 2022 Nov 18.
Recently, single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) have been developed to separately measure transcriptomes and chromatin accessibility profiles at the single-cell resolution. However, few methods can reliably integrate these data to perform regulatory network analysis. Here, we developed integrated regulatory network analysis (IReNA) for network inference through the integrated analysis of scRNA-seq and scATAC-seq data, network modularization, transcription factor enrichment, and construction of simplified intermodular regulatory networks. Using public datasets, we showed that integrated network analysis of scRNA-seq data with scATAC-seq data is more precise to identify known regulators than scRNA-seq data analysis alone. Moreover, IReNA outperformed currently available methods in identifying known regulators. IReNA facilitates the systems-level understanding of biological regulatory mechanisms and is available at https://github.com/jiang-junyao/IReNA.
最近,单细胞RNA测序(scRNA-seq)和利用测序技术进行转座酶可及染色质的单细胞分析(scATAC-seq)已被开发出来,用于在单细胞分辨率下分别测量转录组和染色质可及性图谱。然而,很少有方法能够可靠地整合这些数据以进行调控网络分析。在这里,我们开发了整合调控网络分析(IReNA),通过对scRNA-seq和scATAC-seq数据的整合分析、网络模块化、转录因子富集以及简化模块间调控网络的构建来进行网络推断。使用公共数据集,我们表明,将scRNA-seq数据与scATAC-seq数据进行整合网络分析比单独分析scRNA-seq数据更精确地识别已知调节因子。此外,IReNA在识别已知调节因子方面优于目前可用的方法。IReNA有助于从系统层面理解生物调控机制,可在https://github.com/jiang-junyao/IReNA获取。