Graph-based iterative Group Analysis enhances microarray interpretation
Graph-based iterative Group Analysis enhances microarray interpretation
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DOI：
10.1186/1471-2105-5-100
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发表时间：
2004-07-23
影响因子：
3
通讯作者：
Herzyk, P
中科院分区：
文献类型：
--
作者：
Breitling, R;Amtmann, A;Herzyk, P
背景资料：进行基因表达实验后最耗时的任务之一是通过鉴定差异表达基因之间的生理学重要关联来对结果进行生物学解释。很大一部分相关的功能性证据可以用图形的形式表示，如：G.代谢和信号传导途径、蛋白质相互作用图谱、共享的基因本体注释或文献共引关系。这样的图很容易从可用的基因组注释数据构建。生物学解释的问题可以被描述为识别显示基因表达的最重要模式的子图。我们应用了基于图的迭代组分析（伊加）方法的扩展，以获得任何证据图中感兴趣的子图的统计上严格的识别结果：我们通过将其应用于DeRisi等人的经典酵母二次移位实验来验证基于图的迭代组分析（GiGA），使用基因本体和代谢网络信息。GiGA可靠地识别和总结了原始出版物中讨论的所有生物过程。检测到的子图的可视化允许方便的探索结果。该方法还确定了几个过程，没有在原来的文件，但有明显的相关性酵母饥饿responsibility.Conclusions：GiGA提供了一个快速和灵活的划定最有趣的领域在一个微阵列实验，并导致了相当大的速度和改进的解释过程。
Background: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e. g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph.Results: We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift experiment of DeRisi et al., using GeneOntology and metabolic network information. GiGA reliably identified and summarized all the biological processes discussed in the original publication. Visualization of the detected subgraphs allowed the convenient exploration of the results. The method also identified several processes that were not presented in the original paper but are of obvious relevance to the yeast starvation response.Conclusions: GiGA provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process.