IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles
Junyao Jiang,
Pin Lyu,
Jinlian Li,
Sunan Huang,
Jiawang Tao,
Seth Blackshaw,
Jiang Qian,
Jie Wang
Affiliations
Junyao Jiang
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
Pin Lyu
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Jinlian Li
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
Sunan Huang
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
Jiawang Tao
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
Seth Blackshaw
Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Jiang Qian
Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Jie Wang
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; State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; China-New Zealand Joint Laboratory on Biomedicine and Health, Guangzhou 510530, China; Corresponding author
Summary: 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.
