PSF toolkit: an R package for pathway curation and topology-aware analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-throughput genomic data analysis often uses Over-Representation Analysis/Gene Set Enrichment Analysis (ORA/GSEA).
• Topology-based pathway analysis, while more informative, is underutilized due to accessibility and quality issues with pathway topologies.

Purpose of the Study:

• To introduce the Pathway Signal Flow (PSF) toolkit R package for accessible and flexible topology-aware pathway analysis.
• To integrate pathway curation with topology-based analysis, addressing limitations of existing methods.

Main Methods:

• Developed the PSF toolkit R package with interactive and command-line tools for pathway importation, correction, and modification.
• Curated 36 KEGG signaling pathways and performed comparative analyses with ORA and Signaling Pathway Impact Analysis (SPIA).

Main Results:

• The PSF toolkit effectively identifies ORA-enriched pathways and provides detailed branch-level information.
• PSF analysis is cut-off free and less susceptible to threshold variability compared to SPIA.
• Demonstrated enhanced quality, flexibility, and accessibility in topology-aware pathway analysis.

Conclusions:

• The PSF toolkit offers an integrated solution for pathway curation and topology-based signal flow analysis.
• Empowers researchers with tools for comprehensive pathway analyses across diverse applications.
• Improves upon traditional ORA/GSEA and topology-based methods like SPIA.