TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell...

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Understanding gene regulatory networks (GRNs) is crucial for deciphering cellular processes.
• Current bulk transcriptomics methods for GRN inference require extensive time points, are limited in gene scope, and struggle with causal relationship detection.
• Single-cell RNA sequencing (scRNAseq) offers high-resolution data but presents challenges for GRN reconstruction.

Purpose of the Study:

• To develop a novel computational approach for reconstructing GRNs from scRNAseq data.
• To improve the accuracy and scale of GRN inference, particularly in detecting causal relationships.
• To identify key regulators and gene regulatory cascades in complex biological systems.

Main Methods:

• Proposed a new method named TENET (Transfer Entropy Network) for GRN reconstruction.
• Utilized transfer entropy (TE) to quantify causal relationships between genes.
• Applied TENET to scRNAseq datasets for large-scale GRN prediction.

Main Results:

• TENET demonstrated superior performance compared to existing GRN reconstructors in identifying key regulators from public datasets.
• Successfully identified critical transcriptional factors in embryonic stem cells (ESCs) and during cardiomyocyte reprogramming, where other methods failed.
• Validated that genes with higher predicted TE values by TENET are more influenced by regulator perturbation and known target genes exhibit significantly higher TE.

Conclusions:

• TENET effectively reconstructs gene regulatory networks from scRNAseq data, outperforming current methods.
• The method accurately identifies key regulators and causal relationships, advancing the study of gene regulation.
• Identified Nme2 as a novel culture condition-specific stem cell factor using TENET.