A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease...

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene set enrichment analysis (GSEA) detects coordinated gene expression changes within predefined gene sets, offering more reproducible results than individual gene analysis.
• GSEA is valuable in human disease studies, but small sample sizes and lack of public data limit its power to identify disease-relevant pathways.
• Current methods struggle with low statistical power in disease studies with limited sample sizes.

Purpose of the Study:

• To develop a novel joint gene set analysis framework to enhance the power of identifying enriched gene sets.
• To improve pathway detection by integrating multiple similar disease datasets.
• To provide a robust method for analyzing limited disease study data.

Main Methods:

• Developed a novel joint gene set analysis statistical framework.
• Integrated multiple similar disease datasets to increase analytical power.
• Conducted comprehensive simulation studies and applied the framework to real disease datasets.

Main Results:

• The proposed framework significantly outperformed single dataset analysis and a meta-analysis method in identifying enriched pathways, evidenced by higher AUC scores.
• Application to real datasets retained previously identified gene sets and exclusively identified up to 200% more disease-related gene sets.
• Demonstrated improved identification power through shared information from similar diseases.

Conclusions:

• The novel joint gene set analysis framework effectively enhances the power to detect disease-related pathways.
• This approach is particularly beneficial for studies with small sample sizes and limited public data availability.
• Researchers can leverage this framework to better explore public datasets and uncover crucial biological insights in disease research.