scGET

scGET computes single-cell graph entropy from scRNA-seq data to quantify gene regulatory network stability and predict critical cell fate transitions during early embryonic development.

Key Features:

• Single-Cell Graph Entropy (SGE): Transforms scRNA-seq gene expression into local network entropy to produce the Single-cell Graph Entropy (SGE) metric that characterizes network stability and criticality.
• Critical Transition Prediction: Quantifies SGE values to detect signals indicative of impending cell fate or lineage commitment at single-cell resolution.
• Identification of "Dark Genes": Identifies genes that are not differentially expressed but show sensitivity to changes in SGE and highlights their downstream targets.
• Application Across Datasets: Has been applied to five distinct scRNA-seq datasets of embryonic differentiation to predict impending cell fate transitions.
• Network Perspective Analysis: Analyzes scRNA-seq data from a network perspective to track dynamics of cell differentiation via gene-gene associations.

Scientific Applications:

• Embryonic Development Research: Predicts critical transitions in cell fate commitment to inform studies of early embryonic differentiation.
• Gene Regulatory Network Analysis: Quantifies network stability and criticality to identify key regulatory interactions within gene regulatory networks.
• Biomarker Discovery: Identifies "dark genes" and their downstream targets as candidate biomarkers related to cell development and differentiation.

Methodology:

Transform scRNA-seq gene expression into a local network entropy framework, calculate SGE values to quantify gene regulatory network stability and criticality, detect signals of impending cell fate transitions, and identify genes sensitive to SGE changes (dark genes) along with their downstream targets.