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Dual KS: Defining Gene Sets with Tissue Set Enrichment Analysis
Yarong Yang1, Eric J Kort, Nader Ebrahimi
1These authors contributed equally to this work.
Cancer Informatics
|February 12, 2010
Summary
Dual-KS (DKS) is a novel method that identifies class-specific gene signatures from microarray data. This efficient approach yields parsimonious signatures for accurate sample classification.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Gene set enrichment analysis (GSEA) reduces microarray data dimensionality for biological inference.
- This study inverts GSEA to identify class-specific gene signatures.
- The Dual-KS (DKS) methodology utilizes the Kolmogorov-Smirnov approach.
Purpose of the Study:
- To develop an efficient analytic methodology for identifying class-specific gene signatures.
- To apply the Kolmogorov-Smirnov approach for defining gene signatures and classifying samples.
- To introduce the Dual-KS (DKS) method for enhanced microarray data analysis.
Main Methods:
- Inversion of the GSEA process.
- Application of the Kolmogorov-Smirnov (KS) approach for signature definition and sample classification.
- Development of the Dual-KS (DKS) algorithm.
Main Results:
- DKS identified optimal gene signatures that were smaller than those from other methods in 5 out of 10 datasets.
- DKS demonstrated a lower estimated error rate compared to random forest in 4 out of 10 datasets.
- DKS exhibited comparable performance to other benchmarked algorithms.
Conclusions:
- DKS is an efficient methodology for identifying parsimonious gene signatures in microarray studies.
- DKS facilitates accurate sample classification based on identified gene signatures.
- The DKS algorithm is accessible as the dualKS package within the bioconductor project for R.
