RT Journal Article
SR Electronic
T1 IReNA: integrated regulatory network analysis of single-cell transcriptomes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.22.469628
DO 10.1101/2021.11.22.469628
A1 Junyao Jiang
A1 Seth Blackshaw
A1 Jiang Qian
A1 Jie Wang
YR 2021
UL http://biorxiv.org/content/early/2021/11/23/2021.11.22.469628.abstract
AB While single-cell RNA sequencing (scRNA-seq) is widely used to profile gene expression, few methods are available to infer gene regulatory networks using scRNA-seq data. Here, we developed and extended IReNA (Integrated Regulatory Network Analysis) to perform regulatory network analysis using scRNA-seq profiles. Four features are developed for IReNA. First, regulatory networks are divided into different modules which represent distinct biological functions. Second, transcription factors significantly regulating each gene module can be identified. Third, regulatory relationships among modules can be inferred. Fourth, IReNA can integrate ATAC-seq data into regulatory network analysis. If ATAC-seq data is available, both transcription factor footprints and binding motifs are used to refine regulatory relationships among co-expressed genes. Using public datasets, we showed that integrated network analysis of scRNA-seq data with ATAC-seq data identified a higher fraction of known regulators than scRNA-seq data alone. Moreover, IReNA provided a better performance of network analysis than currently available methods. Beyond the reconstruction of regulatory networks, IReNA can modularize regulatory networks, and identify key regulators and significant regulatory relationships for modules, facilitating the systems-level understanding of biological regulatory mechanisms. The R package IReNA is available at https://github.com/jiang-junyao/IReNA.Key pointsWe have developed IReNA, a new method for reconstructing regulatory networks using either scRNA-seq and ATAC-seq data or scRNA-seq data alone.IReNA can establish modular regulatory networks to identify key regulatory factors and statistically significant regulatory relationships among modules.Through analyzing three public scRNA-seq datasets, IReNA shows better performance on identifying known regulators than Rcistarget, the most widely used alternative method.Competing Interest StatementThe authors have declared no competing interest.