IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles.
IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles.
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DOI：
10.1016/j.isci.2022.105359
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发表时间：
2022-11-18
期刊：
影响因子：
5.8
通讯作者：
Wang, Jie
中科院分区：
文献类型：
--
作者：
Jiang, Junyao;Lyu, Pin;Li, Jinlian;Huang, Sunan;Tao, Jiawang;Blackshaw, Seth;Qian, Jiang;Wang, Jie
最近，单细胞 RNA 测序 (scRNA-seq) 和使用测序进行转座酶可及染色质的单细胞测定 (scATAC-seq) 已被开发出来，可以在单细胞分辨率下单独测量转录组和染色质可及性概况。然而，很少有方法可以可靠地整合这些数据来执行监管网络分析。在这里，我们通过 scRNA-seq 和 scATAC-seq 数据的集成分析、网络模块化、转录因子富集和简化的模块间调节网络的构建，开发了用于网络推理的集成调节网络分析（IRENA）。使用公共数据集，我们表明 scRNA-seq 数据与 scATAC-seq 数据的集成网络分析比单独的 scRNA-seq 数据分析更能准确地识别已知的调控因子。此外，IReNA 在识别已知调节因子方面优于目前可用的方法。 IReNA 促进了对生物调控机制的系统级理解，可在 https://github.com/jian-junyao/IReNA 获取。 IReNA 利用单细胞 RNA-seq 和 ATAC-seq 数据推断调控网络 IReNA 建立模块化调控网络，识别关键调控因子 IReNA 专门在模块之间构建简化的调控网络 应用于公共数据集，IReNA 在网络分析上表现出更好的性能分子网络；转录组学
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. IReNA infers regulatory networks using single-cell RNA-seq and ATAC-seq data IReNA establishes modular regulatory networks to identify key regulators IReNA specifically constructs simplified regulatory networks among modules Applying to public datasets, IReNA shows a better performance on network analysis Biochemistry; Molecular network; Transcriptomics
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影响因子：
14.9
作者：
Imrichová H;Hulselmans G;Atak ZK;Potier D;Aerts S
通讯作者：
Aerts S
DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
8
作者：
Chen, Tianyi;Oh, Sehhoon;Diehl, Anna Mae
通讯作者：
Diehl, Anna Mae