iScienceBioinformaticsPeer-reviewed journal article
IReNA: integrated regulatory network analysis of single-cell transcriptomes
Junyao JiangiD, Pin LyuiD, Jinlian LiiD, Sunan Huang, Jiawang TaoiD, Seth BlackshawiD, Jiang X. QianiD, Jie WangiD
Abstract
Although single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) have been widely used, few methods can reliably integrate these data to perform regulatory network analysis. Here, we developed IReNA (Integrated Regulatory Network Analysis) for network inference through 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 and scATAC-seq data using 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.
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 40
- FWCI
- 2.69
- Field percentile
- top 8%
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