P-SSN

P-SSN constructs sample-specific molecular networks using partial correlations and a reference dataset to retain direct molecular interactions for individual-level biological analysis.

Key Features:

• Partial Correlation-Based Network Construction: P-SSN calculates partial correlations to infer direct interactions and exclude indirect associations in sample-level networks.
• Single-Sample Network Inference: P-SSN operates on individual sample molecular profiles rather than requiring aggregated datasets.
• Reference Dataset Integration: P-SSN uses a reference dataset as a baseline to contextualize single-sample networks and improve inference accuracy.
• Driver Mutation Gene Prediction: P-SSN predicts driver mutation genes (DMGs) from single-sample network information.
• Network Distance Calculation: P-SSN computes a network distance metric between samples to quantify similarity for comparison and classification.
• Subtype Identification and Single-Cell Classification: P-SSN supports identification of disease subtypes and classification of single cells based on sample-specific network structures.

Scientific Applications:

• Disease Characterization: Constructing sample-specific networks to reveal molecular underpinnings of individual diseases.
• Personalized Medicine Development: Enabling tailoring of medical interventions by analyzing individual sample networks.
• Driver Mutation Identification: Identifying candidate driver mutation genes from single-sample data.
• Disease and Cell-Type Comparison via Network Distance: Comparing and classifying complex diseases and cellular subtypes using network distance.
• Tumor and Single-Cell Data Analysis: Application to Cancer Genome Atlas tumor datasets and single-cell datasets for network-based analyses.

Methodology:

Constructs a network for each sample by calculating partial correlations between molecular entities using a reference dataset as a baseline.