Ontology visualization methods—a survey
ACM Computing Surveys (CSUR)
Gene Ontology analysis in multiple gene clusters under multiple hypothesis testing framework
Artificial Intelligence in Medicine
Gene Ontology Assisted Exploratory Microarray Clustering and Its Application to Cancer
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
TreeHugger: A New Test for Enrichment of Gene Ontology Terms
INFORMS Journal on Computing
Using Gene Ontology annotations in exploratory microarray clustering to understand cancer etiology
Pattern Recognition Letters
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The Gene Ontology (GO) resource can be used as a powerful tool to uncover the properties shared among, and specific to, a list of genes produced by high-throughput functional genomics studies, such as microarray studies. In the comparative analysis of several gene lists, researchers maybe interested in knowing which GO terms are enriched in one list of genes but relatively depleted in another. Statistical tests such as Fisherýs exact test or Chi-square test can be performed to search for such GO terms. However, because multiple GO terms are tested simultaneously, individual p-values from individual tests do not serve as good indicators for picking GO terms. Furthermore, these multiple tests are highly correlated, usual multiple testing procedures that work under an independence assumption are not applicable. In this paper we introduce a procedure, based on False Discovery Rate (FDR), to treat this correlated multiple testing problem. This procedure calculates a moderately conserved estimator of q-value for every GO term. We identify the GO terms with q-values that satisfy a desired level as the significant GO terms. This procedure has been implemented into the GoSurfer software. GoSurfer is a windows based graphical data mining tool. It is freely available at http://www.gosurfer.org