A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Area of Science:

• Computational biology
• Bioinformatics
• Systems biology

Background:

• Pathway analysis is crucial for understanding complex disease phenotypes, offering better interpretability and statistical power than gene-level methods.
• Multi-omics data integration is increasingly vital for discovering novel pathways and biomarkers by providing multiple biological views.
• Existing methods for multi-omics pathway analysis vary in their approaches to data aggregation and biological context interpretation.

Approach:

• This article reviews 32 pathway analysis methods designed for multi-omics and multi-cohort data.
• The review assesses methods based on availability, implementation, assumptions, supported omics types, databases, and integration strategies.
• A comprehensive evaluation of each method's practicality, strengths, and weaknesses is provided.

Key Points:

• The survey details 32 pathway analysis tools for multi-omics and multi-cohort data.
• Methods are evaluated for their technical specifications, biological context integration, and practical utility.
• The review highlights challenges and future directions in multi-omics pathway analysis.

Conclusions:

• This comprehensive examination assists researchers in selecting appropriate multi-omics pathway analysis tools.
• The review provides a critical assessment of current methods, guiding future tool development.
• Identifying suitable tools is essential for advancing our understanding of disease mechanisms and identifying therapeutic targets.