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Updated: Sep 15, 2025
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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
Published on: August 16, 2017
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An R package for survival-based gene set enrichment analysis
Xiaoxu Deng1, Jeffrey Thompson1,2
1Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, United States of America.
Peerj
|July 15, 2025
Summary
Survival-based Gene Set Enrichment Analysis (SGSEA) identifies biological functions linked to disease survival. This new R package and Shiny app utilize hazard ratios to find mortality-associated pathways, aiding cancer research.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Functional enrichment analysis typically assesses experimental differences, not direct links to health outcomes like survival.
- Understanding transcriptomic variation's relationship with survival is crucial for disease research.
Purpose of the Study:
- To introduce Survival-based Gene Set Enrichment Analysis (SGSEA) for identifying biological functions associated with disease survival.
- To develop and present an R package and Shiny app for performing SGSEA.
Main Methods:
- SGSEA adapts Gene Set Enrichment Analysis (GSEA) by using hazard ratios instead of log-fold change to rank genes.
- The method was demonstrated using a study of kidney renal clear cell carcinoma (KIRC).
Main Results:
- Pathways enriched with genes showing increased transcription linked to mortality (NES > 0, adjusted p-value < 0.15) were identified.
- These enriched pathways were previously associated with KIRC survival, validating the SGSEA approach.
Conclusions:
- SGSEA offers a valuable method for rapidly identifying disease-variant pathways impacting survival.
- The developed R package and Shiny app provide accessible tools for researchers to supplement standard GSEA.
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