SigHotSpotter

SigHotSpotter predicts signaling pathway hotspots from single-cell RNA sequencing (scRNA-seq) by integrating signaling and transcriptional networks to identify regulatory nodes that control cell subpopulation phenotypes in contexts such as disease and aging.

Key Features:

• Integration of Networks: Combines signaling and transcriptional network information to identify critical points that govern the stability of cell subpopulation phenotypes.
• Hotspot Prediction: Predicts regulatory hotspots within signaling pathways that are crucial for maintaining stable cellular phenotypes.
• Predictive Accuracy: Demonstrates predictive accuracy validated by experimental data across multiple cellular systems.

Scientific Applications:

• Cellular Rejuvenation: Identifies signaling hotspots to inform targeted interventions that modulate cell subpopulation phenotypes relevant to aging and disease.
• Systematic Prediction from scRNA-seq: Enables systematic prediction of signaling hotspots from scRNA-seq data to aid analysis of complex biological processes and therapeutic design.

Methodology:

Integrates signaling pathway data with transcriptional networks derived from single-cell RNA sequencing (scRNA-seq) to identify regulatory hotspots controlling cell subpopulation phenotypes.