ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.

Area of Science:

• Genomics
• Computational Biology
• Systems Biology

Background:

• Gene regulatory networks (GRNs) are crucial for cellular function, but inferring cell-specific GRNs remains challenging.
• Existing methods struggle to integrate diverse single-cell data types like single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq).

Purpose of the Study:

• To develop a novel algorithm, ScReNI, for inferring gene regulatory networks at the single-cell level.
• To enable the integration of paired or unpaired scRNA-seq and scATAC-seq data for robust network inference.
• To identify cell-specific regulators and enhance understanding of single-cell regulatory mechanisms.

Main Methods:

• Developed ScReNI, a novel algorithm for single-cell regulatory network inference.
• Utilized a nearest neighbors algorithm to identify neighboring cells.
• Employed a modified random forest to infer nonlinear regulatory relationships between gene expression and chromatin accessibility.

Main Results:

• ScReNI accurately infers cell-specific regulatory relationships by integrating gene expression and chromatin accessibility data.
• Demonstrated superior performance over existing methods in network-based cell clustering.
• Successfully identified cell-enriched regulators from cell-specific networks.

Conclusions:

• ScReNI provides a powerful tool for inferring cell-specific gene regulatory networks and identifying key regulators.
• The algorithm facilitates deeper insights into the regulatory mechanisms governing diverse biological processes at the single-cell level.
• ScReNI is publicly available, promoting further research in single-cell genomics and computational biology.