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

机译:PCA2GO:一种基于多元统计的新方法用于识别高度表达的GO术语

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摘要

BackgroundSeveral 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.
机译:背景技术已经开发了多种工具来探索和搜索基因本体论(GO)数据库,从而可以进行有效的GO富集分析和GO树可视化。但是,在复杂数据集中识别高度特定的GO术语相对复杂,并且通过简单的表格或饼图显示GO术语分配和GO富集分析并不是最佳方法。不显示有价值的信息,例如单个GO术语在GO树内的层次位置(拓扑顺序),或在一组复杂的生物实验中的富集。基于GO树级别的饼图本身就是一维图,无法正确或有效地表示所研究生物系统的层次特异性。

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