VennMaster:: Area-proportional Euler diagrams for functional GO analysis of microarrays
VennMaster:: Area-proportional Euler diagrams for functional GO analysis of microarrays
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DOI：
10.1186/1471-2105-9-67
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发表时间：
2008-01-29
影响因子：
3
通讯作者：
Weinstein, John N.
中科院分区：
文献类型：
--
作者：
Kestler, Hans A.;Mueller, Andre;Weinstein, John N.
背景：微阵列实验产生大量数据。差异表达基因的功能背景可以通过GoMiner查询基因本体（GO）数据库来评估。有向非循环图表示用于描述富含差异表达基因的GO类别，很难解释，并且根据特定分析，可能不太适合制定新的假设。因此，需要额外的图形方法来增强GO graphical representation.Results：我们提出了一种替代的可视化方法，面积成比例的欧拉图，显示在一个单一的图，以支持生物学假设制定与半定量的大小信息集的关系。集合和交集的基数由面积比例欧拉图及其相应的图形（圆形或多边形）相交面积表示。最佳比例表示使用群和进化优化algorithm.Conclusion：VennMaster的面积比例欧拉图有效地结构和可视化的GO分析的结果，通过指示在何种程度上标记的基因共享不同的类别。除了降低输出的复杂性之外，可视化还有助于从共享差异表达基因的看似不相关的类别的分析中生成新的假设。
Background: Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology ( GO) database via GoMiner. Directed acyclic graph representations, which are used to depict GO categories enriched with differentially expressed genes, are difficult to interpret and, depending on the particular analysis, may not be well suited for formulating new hypotheses. Additional graphical methods are therefore needed to augment the GO graphical representation.Results: We present an alternative visualization approach, area-proportional Euler diagrams, showing set relationships with semi-quantitative size information in a single diagram to support biological hypothesis formulation. The cardinalities of sets and intersection sets are represented by area-proportional Euler diagrams and their corresponding graphical ( circular or polygonal) intersection areas. Optimally proportional representations are obtained using swarm and evolutionary optimization algorithms.Conclusion: VennMaster's area-proportional Euler diagrams effectively structure and visualize the results of a GO analysis by indicating to what extent flagged genes are shared by different categories. In addition to reducing the complexity of the output, the visualizations facilitate generation of novel hypotheses from the analysis of seemingly unrelated categories that share differentially expressed genes.