摘要：SummaryRecently, 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 athttps://github.com/jiang-junyao/IReNA.Graphical abstractDisplay OmittedHighlights•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 analysisBiochemistry; Molecular network; Transcriptomics
