Statistical Methods for the Analysis of Functional Genomic Data

随着高通量分子检测技术的出现，生物学家不得不处理 分析高维基因组数据集。虽然统计方法已被提出的问题， 例如这些数据差分表达式，在 在高通量生物数据的统计分析中结合生物学知识， 人类疾病环境。 在这项资助中，我们提出了复杂的高维模型的统计程序的发展， 生物数据，重点是纳入功能生物学知识。 我们提出的方法将在生物学家可用的软件中实现和分发。而 本基金中的主要生物数据实例来自癌症微阵列实验， 这些方法具有普遍性，可用于研究高维基因型-表型 在其他背景下。有鉴于此，我们提出以下目标： 1.开发复杂细胞系统中高维数据建模的分层模型。 2.疾病进展基因鉴定的统计学方法学发展。 3.发展统计方法，评估功能途径的作用， 基因表达和途径数据的整合。 4.用于确定过表达和低表达区域的统计方法学的发展 基于基因表达和染色体定位数据的整合。 5.用方便用户的统计软件传播这些结果。

With the advent of high-throughput molecular assay technologies, biologists are having to deal with the analysis of high-dimensional genomic datasets. While statistical methods have been proposed for issues such as differential expression with these data, relatively little work has been done in terms of incorporating biological knowledge in the statistical analysis of high-throughput biological data in human disease settings. In this grant, we propose the development of statistical procedures for modeling of complex highdimensional biological data with an emphasis towards incorporating functional biological knowledge. The methods we propose will be implemented and distributed in software available to biologists. While the major biological data example in this grant is from a microarray experiment in cancer, the methods proposed here are general and can be developed for studying high-dimensional genotype-phenotype associations in other contexts. Given this, we propose the following aims: 1. Development of hierarchical models for modelling of high-dimensional data in complex cell systems. 2. Development of statistical methodology for the identification of disease progressor genes. 3. Development of statistical methodology for assessing the role of functional pathways based on integration of gene expression and pathway data. 4. Development of statistical methodology for determining regions of overexpression and underexpression based on integration of gene expression and chromosomal location data. 5. Dissemination of these results in user-friendly statistical software.