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

Abstract

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.

Single-cell chromatin accessibility of developing murine pancreas identifies cell state-specific gene regulatory programs

“…To address this gap in knowledge, we next sought to construct a gene regulatory network (GRN) for Acinar, Ductal, and endocrine cells of the Alpha and Beta lineages ( Figure 4A ). We utilized the computational pipeline Integrated Regulatory Network Analysis (IReNA) v2 [48] ( Figure 4B, Figure S4A ), which combines both scRNA-Seq and snATAC-Seq data to predict TF binding of downstream target genes in a cell type-specific manner. First, we performed differential gene expression analysis on our scRNA-Seq dataset to identify genes enriched in each cell type ( Figure S4B, Supplemental table 4 ).…”

Computational approaches to understand transcription regulation in development

“…By adding more modalities, and making GRNs more complex, networks become even more underdetermined. This is why most multi-omics approaches use the new modalities to prune the possible TF-target gene relations, which actually reduces the degrees of freedom [98,122,125,126]. Moreover, one can use time-series data to further prune TF-target gene interactions [169], although time-series multi-omics GRN inference tools are still relatively uncommon [170][171][172][173].…”

“…The large number of cells, however, allows for specialized single cell GRN approaches. These include mutual information in combination with partial information decomposition [121], gene coexpression [122], self organizing maps [123], or a combination of single cell RNA-seq and single cell ATAC-seq coexpression and/or bayesian ridge regression [124][125][126]. Other approaches first order cells by their inferred temporal ordering and then infer the gene-gene relations on this pseudotime, with the assumption that these orderings, also called trajectories, represent cell lineages [127].…”