DGCA

DGCA performs differential gene correlation analysis to detect changes in pairwise gene correlations across experimental or clinical conditions, enabling identification of altered regulatory relationships in biological systems.

Key Features:

• Empirical p-value Calculation: Computes empirical p-values via permutation testing to minimize reliance on parametric distributional assumptions.
• Higher-Order Analyses: Supports measurement of average differences in correlations and multiscale clustering analysis of differential correlation networks.
• Z-Score Based Methodology: Implements a z-score based method for calculating differential correlations, reported in simulation studies to outperform alternative methods.
• Visualization and Interpretation Tools: Provides functions for filtering, processing, saving, visualizing, and interpreting differential correlations across the entire identifier space or selected identifier sets.

Scientific Applications:

• TCGA breast cancer RNA-seq analysis: Applied to TCGA RNA-seq breast cancer data to analyze differential gene correlations across conditions.
• Mutation-associated regulatory changes: Identified changes in regulatory relationships involving TP53 and PTEN associated with inactivating mutations.
• TNBC immune module discovery: Detected an immune-related differential correlation module specific to triple negative breast cancer (TNBC), enabling discovery of candidate signaling pathways, biomarkers, and therapeutic targets.

Methodology:

Empirical p-value computation via permutation testing, z-score based differential correlation calculation, measurement of average differences in correlations, and multiscale clustering analysis.