Single-cell gene regulation network inference by large-scale data integration.

Area of Science:

• Genomics
• Molecular Biology
• Computational Biology

Background:

• Single-cell ATAC-seq (scATAC-seq) is a powerful tool for studying gene regulation at the single-cell level.
• Existing methods struggle to precisely identify cell-type-specific transcription regulator (TR) binding and construct single-cell gene regulatory networks (GRNs).
• Chromatin immunoprecipitation sequencing (ChIP-seq) is a standard method for profiling TR binding sites.

Purpose of the Study:

• To develop an integrative method for inferring single-cell TR activity and targets.
• To enable the construction of single-cell resolution GRNs.
• To improve the accuracy of TR binding activity evaluation and its consistency with TR expression.

Main Methods:

• Integration of scATAC-seq data with a large-scale TR ChIP-seq reference dataset.
• Development of SCRIP, a novel computational method for inferring TR activity and targets.
• Application of a regulatory potential model for identifying TR target genes and building GRNs.

Main Results:

• SCRIP demonstrated superior performance in evaluating TR binding activity compared to existing motif-based methods.
• The method achieved higher consistency between inferred TR activity and matched TR expressions.
• SCRIP successfully identified TR target genes and facilitated the construction of single-cell GRNs.

Conclusions:

• SCRIP accurately infers single-cell TR activity and targets, overcoming limitations of existing methods.
• The method enables the construction of cell-type-specific GRNs at single-cell resolution.
• SCRIP is a valuable tool for cell-type clustering, lineage tracing, and understanding gene regulation in diverse biological systems.