netGO: R-Shiny package for network-integrated pathway enrichment analysis.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Pathway enrichment analysis is crucial for interpreting high-throughput biological data.
• Traditional methods like Fisher's exact test (FET) focus on gene overlap, while network-based tools prioritize interactions.
• A unified approach integrating both overlap and network information is needed.

Purpose of the Study:

• To introduce netGO, an R-Shiny package for network-integrated pathway enrichment analysis.
• To develop a novel framework that combines gene overlap and network topology for improved pathway identification.
• To provide interactive visualizations for exploring enrichment results and network interactions.

Main Methods:

• netGO integrates gene overlap and network information into a single enrichment score.
• It employs adaptive resampling of genes based on network degrees.
• User-interactive visualization of target genes, enriched gene-sets, and their network interactions is provided via R-Shiny and shinyCyJS.

Main Results:

• netGO demonstrated superior performance in benchmark tests using gene expression and genome-wide association study (GWAS) data.
• The tool effectively captured relevant gene-sets, particularly when analyzing a limited number of genes.
• Comparative analysis showed netGO outperforming existing pathway enrichment tools.

Conclusions:

• netGO offers a powerful and intuitive approach for network-integrated pathway enrichment analysis.
• The package enhances the interpretation of biological data by combining gene set overlap and network context.
• netGO provides valuable visualization tools for deeper biological insights and further analysis.