c-CSN

c-CSN constructs conditional cell-specific gene association networks from single-cell RNA sequencing (scRNA-seq) data to identify direct gene interactions and analyze cellular differentiation potential.

Key Features:

• Conditional Cell-Specific Network Construction: Builds a conditional cell-specific network (CCSN) for each cell by removing indirect gene associations to identify direct gene–gene interactions.
• Network-Based Cell Clustering: Enables cell clustering and dimensionality reduction using gene–gene association networks derived from scRNA-seq data.
• Network Flow Entropy (NFE): Calculates network flow entropy to estimate the differentiation potential and potency of individual cells.
• Degree Matrix Transformation: Generates CCSN-based degree matrices compatible with existing single-cell RNA-seq analytical methods.

Scientific Applications:

• Single-Cell Network Analysis: Identifies direct gene interaction networks within individual cells from scRNA-seq datasets.
• Cell Differentiation Analysis: Infers differentiation trajectories and cellular potency using network flow entropy metrics.
• Cellular Heterogeneity Studies: Characterizes heterogeneity and lineage relationships in single-cell transcriptomic data.

Methodology:

c-CSN constructs conditional cell-specific networks using statistical independence principles to remove indirect gene associations from scRNA-seq gene expression data and computes network flow entropy to estimate cellular differentiation potential.