crosslinkWGCNA

crosslinkWGCNA performs weighted gene co-expression network analysis (WGCNA) to identify clusters of highly correlated genes and relate these modules to external sample traits.

Key Features:

• WGCNA R package (Horvath lab): Implements functions from the WGCNA R package developed by Horvath's lab for network construction and module detection.
• Correlation network construction: Constructs gene co-expression correlation networks from expression data.
• Module detection: Detects clusters (modules) of highly correlated genes using hierarchical clustering and module detection algorithms.
• Module summarization: Summarizes modules using module eigengenes and identifies intramodular hub genes.
• Module-trait correlation: Computes correlations between module eigengenes and external sample traits.
• Topological analysis: Calculates topological properties and performs gene selection based on network metrics.
• Visualization: Produces hierarchical gene clustering, color-coded module representations, sample trees, and module-trait correlation plots.
• Data simulation: Supports data simulation functionality provided by the WGCNA package.
• Cross-analysis integration: Integrates multiple WGCNA analyses using correlation-based approaches for comparative analyses.

Scientific Applications:

• Module identification: Identify co-expression modules that capture coordinated gene expression patterns.
• Biomarker and target discovery: Screen networks to nominate candidate biomarkers or therapeutic targets based on module membership and hub genes.
• Trait association studies: Relate gene modules to phenotypic or sample traits via module-trait correlations.
• Domain-specific systems biology: Apply co-expression network analysis in contexts such as cancer research, mouse genetics, yeast genetics, and brain imaging studies.

Methodology:

Built on the WGCNA R package, methods explicitly include constructing correlation networks, detecting modules, selecting genes, calculating topological properties, simulating data, and visualizing results.