Methodology article
Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology
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* Corresponding authors: Yan Zhou zhouy@chgc.sh.cn - LinFang Du dulinfang@yahoo.com
1 College of Life Sciences, Sichuan University, Chengdu 610064, PR China
2 Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai 201203, PR China
3 Department of Microbiology, School of Life Sciences, Fudan University, Shanghai 200433, PR China
BMC Bioinformatics 2009, 10:240 doi:10.1186/1471-2105-10-240
Published: 5 August 2009Abstract
Background
Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment.
Results
We proposed a method for enriching clustered GO terms based on semantic similarity, namely cluster enrichment analysis based on GO (CeaGO), to extend the individual term analysis method. Using an Affymetrix HGU95aV2 chip dataset with simulated gene sets, we illustrated that CeaGO was sensitive enough to detect moderate expression changes. When compared to parent-based individual term analysis methods, the results showed that CeaGO may provide more accurate differentiation of gene expression results. When used with two acute leukemia (ALL and ALL/AML) microarray expression datasets, CeaGO correctly identified specifically enriched GO groups that were overlooked by other individual test methods.
Conclusion
By applying CeaGO to both simulated and real microarray data, we showed that this approach could enhance the interpretation of microarray experiments. CeaGO is currently available at http://chgc.sh.cn/en/software/CeaGO/ webcite.