GOREA: Unbiased Interpretation of Functional Enrichment

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Functional enrichment analysis, including Gene Set Enrichment Analysis (GSEA) and Over-Representation Analysis (ORA), is crucial for interpreting gene expression data.
• Interpreting numerous enriched Gene Ontology Biological Process (GOBP) terms remains a significant challenge.
• Existing tools like simplifyEnrichment often produce overly general or fragmented summaries and do not leverage quantitative metrics for prioritization.

Purpose of the Study:

• To develop GOREA, an improved tool for summarizing GOBP terms derived from GSEA and ORA.
• To enhance biological interpretation and prioritization of enriched terms by integrating quantitative metrics and hierarchical information.
• To provide a more scalable and efficient framework for functional enrichment analysis.

Main Methods:

• GOREA integrates binary cut and hierarchical clustering to group GOBP terms.
• It utilizes the GOBP term hierarchy to identify representative terms within clusters.
• Clusters are ranked using Normalized Enrichment Scores (NES) or gene overlap proportions.
• Results are visualized using ComplexHeatmap, displaying heatmaps with broad and representative GOBP terms.

Main Results:

• GOREA generates more specific and interpretable clusters compared to simplifyEnrichment.
• The tool significantly reduces computational time.
• GOREA effectively identified distinct biological processes in immune-related data.
• It revealed substantial overlap between GOBP terms and cancer hallmark gene sets.

Conclusions:

• GOREA offers a substantial improvement over existing methods for summarizing GOBP terms.
• The tool provides both general and specific biological insights, aiding interpretation.
• GOREA is applicable across diverse biological contexts, including immunology and cancer research.
• It presents a scalable and efficient framework for functional enrichment analysis.