stringgaussnet

stringgaussnet constructs integrated knowledge-based and Gaussian co-expression gene networks from lists of differentially expressed genes by combining STRING protein–protein interaction data (via the STRING API) with co-expression inference using SIMoNe (Single Index Model Network Estimation) and WGCNA (Weighted Gene Co-expression Network Analysis) in R.

Key Features:

• Integration of Knowledge-Based and Co-Expression Networks: Combines knowledge-based networks derived from the STRING database with co-expression networks inferred from expression data using SIMoNe and WGCNA.
• Differential Expression Input: Starts from a provided list of differentially expressed genes, typically derived from high-throughput experiments.
• Protein-Protein Interaction Network Construction: Retrieves protein–protein interaction data from STRING via the STRING API to build knowledge-based interaction networks.
• Inference of Gaussian Networks: Infers Gaussian co-expression networks, identifies gene modules and hub genes using SIMoNe and WGCNA.
• Cytoscape Integration: Provides functionality to import constructed networks into an active Cytoscape session for downstream visualization and analysis.
• R Package Implementation: Implements the described network construction and inference methods as an R package.

Scientific Applications:

• Functional Genomics: Identify key genes and their interactions to elucidate biological pathways affected under specific conditions.
• Disease Mechanism Exploration: Highlight gene networks implicated in disease processes to suggest potential molecular targets.
• Biomarker Discovery: Analyze co-expression patterns to identify candidate diagnostic or prognostic biomarkers.

Methodology:

Input a list of differentially expressed genes; construct protein–protein interaction networks using the STRING API; infer co-expression Gaussian networks and gene modules using SIMoNe and WGCNA; integrate the resulting networks and import them into Cytoscape.