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Cluster Analysis of Dynamic Parameters of Gene Expression

机译:基因表达动态参数的聚类分析

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Cluster analysis has proven to be a valuable statistical method for analyzing whole genome expression data. Although clustering methods have great utility, they do represent a lower level statistical analysis that is not directly tied to a specific model. To extend such methods and to allow for more sophisticated lines of inference, we use cluster analysis in conjunction with a specific model of gene expression dynamics. This model provides phenomenological dynamic parameters on both linear and non-linear responses of the system. This analysis determines the parameters of two different transition matrices (linear and nonlinear) that describe the influence of one gene expression level on another. Using yeast cell cycle microarray data as test set, we calculated the transition matrices and used these dynamic parameters as a metric for cluster analysis. Hierarchical cluster analysis of this transition matrix reveals how a set of genes influence the expression of other genes activated during different cell cycle phases. Most strikingly, genes in different stages of cell cycle preferentially activate or inactivate genes in other stages of cell cycle, and this relationship can be readily visualized in a two-way clustering image. The observation is prior to any knowledge of the chronological characteristics of the cell cycle process. This method shows the utility of using model parameters as a metric in cluster analysis.
机译:聚类分析已被证明是分析整个基因组表达数据的一种有价值的统计方法。尽管聚类方法具有很大的实用性,但它们的确代表了较低级别的统计分析,该分析不直接与特定模型相关联。为了扩展此类方法并允许进行更复杂的推理,我们将聚类分析与基因表达动力学的特定模型结合使用。该模型提供了系统线性和非线性响应的现象学动态参数。该分析确定了描述一个基因表达水平对另一基因表达水平的影响的两个不同过渡矩阵(线性和非线性)的参数。使用酵母细胞周期微阵列数据作为测试集,我们计算了转移矩阵,并将这些动态参数用作聚类分析的指标。此过渡矩阵的层次聚类分析揭示了一组基因如何影响在不同细胞周期阶段激活的其他基因的表达。最引人注目的是,处于细胞周期不同阶段的基因优先激活或失活处于细胞周期其他阶段的基因,并且这种关系可以很容易地在双向聚类图像中可视化。该观察是在对细胞周期过程的时间特征的任何了解之前进行的。该方法显示了在聚类分析中使用模型参数作为度量的实用性。

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