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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >ANALYZING MICROARRAY DATA WITH TRANSITIVE DIRECTED ACYCLIC GRAPHS
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ANALYZING MICROARRAY DATA WITH TRANSITIVE DIRECTED ACYCLIC GRAPHS

机译：带有过渡直接环图的微阵列数据分析

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摘要

Post hoc assignment of patterns determined by all pairwise comparisons in microarray experiments with multiple treatments has been proven to be useful in assessing treat_ment effects. We propose the usage of transitive directed acyclic graphs (tDAG) as the representation of these patterns and show that such representation can be useful in clustering treatment effects, annotating existing clustering methods, and analyzing sample sizes. Advantages of this approach include: (1) unique and descriptive meaning of each cluster in terms of how genes respond to all pairs of treatments; (2) insensitivity of the observed patterns to the number of genes analyzed; and (3) a combinatorial perspective to address the sample size problem by observing the rate of contractible tDAG as the number of replicates increases. The advantages and overall utility of the method in elaborating drug structure activity relationships are exemplified in a controlled study with real and simulated data.
机译：经证实，在多种治疗的微阵列实验中，通过所有成对比较确定的事后分配模式可用于评估治疗效果。我们提出使用传递有向无环图（tDAG）作为这些模式的表示，并表明这种表示可用于聚类处理效果，注释现有聚类方法以及分析样本量。这种方法的优点包括：（1）就基因对所有对治疗的反应方式而言，每个簇的独特和描述性含义; （2）观察到的模式对分析的基因数目不敏感; （3）通过观察重复次数增加时可收缩的tDAG的比率来解决样本量问题的组合视角。该方法在阐述药物结构活性关系方面的优势和整体实用性在具有真实和模拟数据的对照研究中得到了例证。

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