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Abstract：
Many scientific studies, especially in the biomedical sciences, generate data measured simultaneously over a multitude of units, over a period of time, and under different conditions or combinations of factors. Often, an important question of interest asked relates to which units behave similarly under different conditions, but measuring the variation over time complicates the analysis significantly. In this article we address such a problem arising from a gene expression study relating to bone aging, and develop a Bayesian statistical method that can simultaneously detect and uncover signals on three levels within such data: factorial, longitudinal, and transcriptional. Our model framework considers both cluster and time-point-specific parameters and these parameters uniquely determine the shapes of the temporal gene expression profiles, allowing the discovery and characterization of latent gene clusters based on similar underlying biological mechanisms. Our methodology was successfully applied to discover transcriptional networks in a microarray data set comparing the transcriptomic changes that occurred during bone aging in male and female mice expressing one or both copies of the bromodomain (Brd2) gene, a transcriptional regulator which exhibits an age-dependent sex-linked bone loss phenotype.
摘要：
许多科学研究,尤其是在生物医学领域，生成在多个单元上同时测量的数据，在一段时间内,以及在不同条件或因素组合下。通常,一个重要的兴趣问题涉及哪些单位在不同条件下表现相似，但是测量随时间的变化会使分析变得非常复杂。在本文中，我们解决了与骨老化有关的基因表达研究引起的问题，并开发一种贝叶斯统计方法，该方法可以同时检测和发现此类数据中三个级别的信号：阶乘，纵向,和转录。我们的模型框架考虑了簇和时间点特定的参数，这些参数唯一地确定了时间基因表达谱的形状。允许基于类似的潜在生物学机制发现和表征潜在基因簇。我们的方法已成功应用于发现微阵列数据集中的转录网络，比较表达一个或两个溴结构域（Brd2）基因拷贝的雄性和雌性小鼠在骨老化过程中发生的转录组变化。一种转录调节因子，表现出年龄依赖性与性别相关的骨丢失表型。
