ScReNI: single-cell regulatory network inference through integrating scRNA-seq and scATAC-seq data

Single cells exhibit heterogeneous gene expression profiles and chromatin accessibility, measurable separately via single-cell RNA sequencing (scRNA-seq) and single-cell transposase chromatin accessibility sequencing (scATAC-seq). Consequently, each cell possesses a unique gene regulatory network. However, limited methods exist for inferring cell-specific regulatory networks, particularly through the integration of scRNA-seq and scATAC-seq data. Here, we develop a novel algorithm named single-cell regulatory network inference (ScReNI), which leverages k-nearest neighbors and random forest algorithms to integrate scRNA-seq and scATAC-seq data for inferring gene regulatory networks at the single-cell level. ScReNI is built to analyze both paired and unpaired datasets for scRNA-seq and scATAC-seq. Using these two types of single-cell sequencing datasets, we validate that a higher fraction of regulatory relationships inferred by ScReNI are detected by chromatin immunoprecipitation sequencing (ChIP-seq) data. ScReNI shows superior performance in network-based cell clustering when compared to existing single-cell network inference methods. Importantly, ScReNI offers the unique function of identifying cell-enriched regulators based on each cell-specific network. In summary, ScReNI facilitates the inferences of cell-specific regulatory networks and cell-enriched regulators.