IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles

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 int...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:iScience 2022-11, Vol.25 (11), p.105359-105359, Article 105359
Hauptverfasser: Jiang, Junyao, Lyu, Pin, Li, Jinlian, Huang, Sunan, Tao, Jiawang, Blackshaw, Seth, Qian, Jiang, Wang, Jie
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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. [Display omitted] •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
ISSN:2589-0042
2589-0042
DOI:10.1016/j.isci.2022.105359