ANOVA-Based Approaches to Time-Series Microarray Data

描述(申请人提供)：在生物医学研究中考虑转录反应的动态变化时，需要将统计学意义与生物相关性结合起来。为此，将开发基于方差分析(ANOVA)的方法，通过治疗条件内和治疗条件之间的聚焦对比和趋势测试来检测时间进程微阵列数据中的差异表达基因。这些基于方差分析的分析的效率将与经验贝叶斯方法和B-Spline拟合分析进行比较，以综合各种方法的最佳特征的新方法。接下来，从方差分析方法得出的基因列表与已知生物过程之间的统计相关性将被用来揭示在研究苍蝇和小鼠睡眠剥夺的脑组织样本中的重要关系，以及在UAMS-33小鼠细胞培养中影响骨细胞和脂肪细胞分化的因素。最后，将设计验证性实时定量聚合酶链式反应分析方法，并将其应用于具有较高频率时间点采样的UAMS-33小鼠细胞系实验，以建立精细描述该系统时间动力学的图形模型。

DESCRIPTION (provided by applicant): There is a need to integrate statistical significance with biological relevance when considering dynamic changes in transcriptional response in biomedical research. Towards this end, methodologies based on analysis of variance (ANOVA) will be developed to detect differentially expressed genes in time course microarray data through focused contrasts and trend tests within and between treatment conditions. The efficiency of these ANOVA-based analyses will be compared with empirical Bayes methods and B-spline fitting analyses to synthesize new methodologies that incorporate the best features of the various approaches. Next, statistically relevant associations between gene lists derived from the ANOVA method and known biological processes will be used to uncover relationships important in studies of sleep deprivation in brain tissue sampled for flies and mice and factors influencing osteocyte and adipocyte differentiation in UAMS-33 mouse cell cultures. Finally, confirmatory real-time quantitative PCR assays will be designed and applied to the UAMS-33 mouse cell line experiments with higher frequency time point sampling in order to build graphical models that finely characterize the time dynamics of this system.