Dynamic Programming Algorithm for Gene Set Enrichment Analysis
Author Information
Author(s): Andreas Keller, Christina Backes, Hans-Peter Lenhof
Primary Institution: Center for Bioinformatics, Saarland University
Hypothesis
Can a dynamic programming algorithm improve the computation of significance values in Gene Set Enrichment Analysis?
Conclusion
The study presents a dynamic programming algorithm that efficiently computes exact significance values for unweighted Gene Set Enrichment Analysis, avoiding issues associated with nonparametric permutation tests.
Supporting Evidence
- The dynamic programming algorithm significantly reduces computation time compared to traditional permutation tests.
- 1744 out of 6428 biological categories were statistically significant at an alpha level of 0.05.
- The algorithm was integrated into the GeneTrail tool, which is freely available for use.
Takeaway
This study created a new way to quickly find out if certain genes are important in cancer by using a smart computer program instead of a slower method.
Methodology
The algorithm uses dynamic programming to calculate significance values based on running sum statistics from sorted gene lists.
Limitations
The algorithm's performance may vary with the number of genes and categories tested, and it may not handle extremely large datasets efficiently.
Participant Demographics
The study evaluated expression profiles from 5 squamous cell lung cancer patients and their autologous unaffected tissues.
Statistical Information
P-Value
0.00024
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website