Interpreting and visualizing pathway analyses using embedding representations with PAVER.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Omics studies generate vast high-throughput datasets to understand biological traits and conditions.
• Interpreting the long lists of pathways resulting from omics analysis presents a significant challenge.
• Effective tools are needed to distill complex pathway information into actionable biological insights.

Purpose of the Study:

• To introduce PAVER (Pathway Analysis Visualization with Embedding Representations), a novel tool designed for large-scale genomic analysis.
• To address the challenge of interpreting extensive pathway analysis results from omics data.
• To provide intuitive visualizations and curate similar pathways for enhanced biological understanding.

Main Methods:

• PAVER utilizes vector embedding representations to cluster pathways based on similarity.
• It identifies the most representative pathway within each cluster using cosine similarity to the cluster's average embedding.
• The tool integrates multiple pathway analyses and supports any pathway database.

Main Results:

• PAVER effectively curates numerous similar pathways into manageable groups.
• It identifies a single, representative pathway for each curated group, simplifying interpretation.
• The tool generates intuitive, publication-ready visualizations of pathway relationships.

Conclusions:

• PAVER offers a robust solution for interpreting complex omics data by simplifying pathway analysis.
• The tool enhances the identification of relevant biological insights from large-scale genomic studies.
• PAVER's ability to integrate diverse analyses and databases makes it a versatile resource for researchers.