Comparative study on gene set and pathway topology-based enrichment methods.

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Enrichment analysis identifies significantly enriched pathways or gene sets within differentially expressed genes.
• Traditional gene set enrichment ignores pathway interaction knowledge, treating pathways as simple gene lists.
• Pathway topology-based methods integrate pathway structures for a more nuanced analysis.

Purpose of the Study:

• To comparatively investigate gene set and pathway topology-based enrichment approaches.
• To evaluate the performance of these methods using simulations and real-world benchmark datasets.

Main Methods:

• Compared three gene set and four pathway topology-based enrichment methods.
• Conducted two extensive simulation studies.
• Utilized a benchmark of 36 real datasets, ensuring consistent pathway input for all methods.

Main Results:

• Both method types demonstrated comparable pathway detection ability on benchmark data.
• In simulations with overlapping KEGG pathways, topology-based methods did not outperform gene set methods.
• In simulations with non-overlapping pathways, topology-based methods achieved higher accuracy but lower sensitivity.

Conclusions:

• Pathway topology methods perform better with non-overlapping pathways, but gene set approaches may suffice in realistic scenarios.
• Further research and benchmark data are needed to fully assess the value and cost of incorporating pathway topology.
• Both enrichment analysis approaches require improvement to effectively handle pathway overlaps.