pyPARAGON

pyPARAGON combines network propagation with graphlets to analyze and integrate multi-omic data in the context of signaling networks, enhancing accuracy and minimizing nonspecific interactions by utilizing network topology rather than pairwise protein connections.

Key Features:

• Network propagation with graphlets: Integrates multi-omic data using graphlet-guided network propagation rather than relying on pairwise connections among proteins.
• Node propagation: Propagates omics hits through signaling networks with improved accuracy over state-of-the-art approaches.
• Edge inference: Infers context-specific interactions within sparse omics datasets in complex interactomes.
• Cancer driver network discovery: Identifies dysregulated and altered signaling components in disease states and drug perturbations.
• Network-based patient stratification: Integrates phosphoproteomic data with interactomes to reveal tumor-specific signaling pathways.

Scientific Applications:

• Disease network construction: Constructs signaling networks to identify causal relationships between omics hits in disease states.
• Cancer driver network discovery: Discovers cancer driver networks through comprehensive evaluation on benchmark signaling pathways.
• Breast cancer tumor stratification: Stratifies patient tumors by integrating phosphoproteomic data from breast cancer tumors with the interactome to demonstrate tumor-specific signaling pathways.

Methodology:

pyPARAGON implements PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN, combining network propagation with graphlets to analyze and integrate multi-omic data in signaling networks.