Tan Qihua, Thomassen Mads, Burton Mark, Mose Kristian Fredløv, Andersen Klaus Ejner, Hjelmborg Jacob, Kruse Torben
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J Integr Bioinform. 2017 Jun 6;14(2):/j/jib.2017.14.issue-2/jib-2017-0011/jib-2017-0011.xml. doi: 10.1515/jib-2017-0011.
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health.
由于在时间进程实验中基因表达模式复杂，在微阵列研究中对复杂的时间进程模式进行建模是一个具有挑战性的问题。我们引入了广义相关系数，并提出了一种用于检测、测试和聚类异质时间进程基因表达模式的组合方法。该方法的应用识别出与参数分析高度一致的非线性时间进程模式。我们得出结论，广义相关分析中的非参数性质可能是分析微阵列时间进程数据以及探索组学数据中复杂关系以研究其与疾病和健康关联的有用且高效的工具。
