Learning Objectives
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# Learning Objectives - Use network visualization tools - Analyze networks with Cytoscape ## 1. Network Visualization Principles Layout algorithms: Force-directed (PPI, co-expression - reveals clusters), Hierarchical (regulatory networks - shows levels), Circular (pathway maps).
Learning Objectives
- Use network visualization tools
- Analyze networks with Cytoscape
1. Network Visualization Principles
Layout algorithms: Force-directed (PPI, co-expression - reveals clusters), Hierarchical (regulatory networks - shows levels), Circular (pathway maps).
Visual encoding: Node size = degree/centrality, color = expression level, edge width = confidence.
2. Cytoscape Basics
Import network (SIF, XGMML), import node attributes, set visual style, run Network Analyzer for degree distribution and centrality. Use Apps: MCODE (complex detection), cytoHubba (hub identification), ClueGO (GO enrichment).
3. Other Tools
- Gephi: Large network visualization and exploration
- iGraph (R/Python): Programmatic network analysis
- NetworkX (Python): Comprehensive Python network library
- BioLayout Express3D: 3D visualization for large biological networks
Q1: What layout works best for PPI networks?Force-directed (Kamada-Kawai). Nodes repel each other, connected nodes attract. Reveals clusters, hub-and-spoke structure naturally. Q2: How do you visualize gene expression on a network?Import expression values as node attributes. Map to node color (continuous: red=up, white=unchanged, blue=down). Interactive exploration reveals expression patterns in network context. Q3: What does the MCODE app find?Densely connected regions (= high clustering coefficient). These correspond to protein complexes and functional modules in PPI networks. Join Discord PreviousNetwork Evolution & DynamicsNextSignaling & Neural Networks