Improving detection of differentially expressed gene sets by applying cluster enrichment analysis to Gene Ontology

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.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.Identifying differentially expressed genes (DEGs) from microarray experiments enables researchers to elucidate related biological processes. In addition to studies focused on individual genes such as SAM[1], statistical techniques have been successfully employed to determine whether predefined groups, for example those in Gene Ontology (GO) [2], or in a metabolic pathway, are differentially expressed. There are two main statistical testing approaches: individual gene analysis (IGA) [3,4] and Gene Set Analysis (GSA) [5]. IGA is performed in two steps: first, genes of interest are selected using a cutoff threshold, and the enriched biological categories are gained by statistically testing these genes against the background: typically all genes in the category (e.g., Fisher's exact test). The major limitation of IGA is that the result is significantly affected by an arbitrarily chosen cutoff in the first step. Hence, the GSA approach was developed to address this issue. GSA methods calculate a score based on all the genes within the gene set. Since it is fr