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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 A Thompson1
1University of Kansas Medical Center.
Research Square
|October 16, 2023
Summary
Survival-based Gene Set Enrichment Analysis (SGSEA) helps identify biological functions linked to disease survival by using hazard ratios. This method, available in an R package and Shiny app, aids in discovering disease-variant pathways for kidney renal clear cell carcinoma.
Area of Science:
- Bioinformatics
- Genomics
- Translational Medicine
Background:
- Functional enrichment analysis typically assesses experimental differences.
- Understanding transcriptomic variation's link to survival outcomes is crucial in disease research.
- Standard Gene Set Enrichment Analysis (GSEA) uses log-fold change, not ideal for survival data.
Purpose of the Study:
- Introduce Survival-based Gene Set Enrichment Analysis (SGSEA) for linking biological functions to disease survival.
- Develop an R package and Shiny App named SGSEA for this novel analysis.
- Demonstrate SGSEA's utility using a kidney renal clear cell carcinoma (KIRC) dataset.
Main Methods:
- Adapted Gene Set Enrichment Analysis (GSEA) by replacing log-fold change with hazard ratio for gene ranking.
- Developed an R package and Shiny App (SGSEA) for performing survival-based gene set enrichment analysis.
- Applied SGSEA to a KIRC dataset to identify survival-associated pathways.
Main Results:
- Identified pathways enriched with genes showing increased transcription linked to mortality (NES > 0, adjusted p-value < 0.15).
- Confirmed that these enriched pathways are previously associated with KIRC survival.
- Demonstrated the value of SGSEA in identifying biologically relevant pathways for disease survival.
Conclusions:
- SGSEA provides a valuable method for identifying disease-variant pathways associated with patient survival.
- The SGSEA R package and Shiny App offer a convenient tool for researchers.
- This approach complements standard GSEA by incorporating survival data for enhanced biological insight.
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