PCA2GO: a new multivariate statistics based method to identify highly expressed GO-Terms

Background: Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied. Results: Here we present a new method, which we name PCA2GO, capable of GO analysis using complex multidimensional experimental settings. We employed principal component analysis (PCA) and developed a new score, which takes in...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Marc Bruckskotten, Mario Looso, Franz Cemic, Anne Konzer, Jurgen Hemberger, Markus Kruger, Thomas Braun
• Keywords: BMCBI 2010, Enrichment Analysis, GO Terms, GO Tree