PCA2GO: a new multivariate statistics based method to identify highly expressed GO-Terms |
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| Authors: | Marc Bruckskotten Mario Looso Franz Cemi? Anne Konzer Jürgen Hemberger Marcus Krüger Thomas Braun |
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| Institution: | (1) Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany;(2) Institute for Biochemical Engineering and Analytics, University of Applied Sciences Giessen-Friedberg, 35390 Giessen, Germany |
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| Abstract: | 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. |
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