TENET

TENET reconstructs gene regulatory networks from pseudo-time ordered single-cell RNA sequencing (scRNAseq) data using transfer entropy to identify causal regulatory interactions.

Key Features:

• Transfer Entropy Methodology: Employs transfer entropy (TE) to quantify directional information transfer between gene pairs and infer causal relationships.
• Single Cell Resolution: Leverages pseudo-time ordered single-cell transcriptomic data to infer dynamic regulatory directionality at single-cell resolution.
• Identification of Key Regulators: Prioritizes transcriptional factors and regulatory elements, demonstrating identification of regulators in embryonic stem cells (ESCs) and during direct cardiomyocyte reprogramming.
• Validation through Perturbation Analysis: Validates predicted high-TE genes by showing these genes are more influenced by perturbation of their predicted regulators.
• Culture Condition Specific Insights: Detects culture condition-specific regulatory factors, exemplified by identification of Nme2.

Scientific Applications:

• GRN Reconstruction from scRNAseq: Reconstruction of gene regulatory networks from pseudo-time ordered scRNAseq to infer causal gene-gene interactions.
• Dynamic and Developmental Biology: Analysis of dynamic cellular processes and developmental trajectories at single-cell resolution.
• Disease Modeling and Target Discovery: Identification of key regulatory factors and candidate therapeutic targets in disease models.
• Perturbation and Condition-Specific Analysis: Assessment of regulator impact via perturbation analyses and discovery of condition-specific factors such as Nme2.

Methodology:

Computes transfer entropy between gene pairs from pseudo-time ordered scRNAseq data to predict high-TE genes and validates regulatory relevance by comparing target responses to perturbation of predicted regulators.