Statistical Methods for the Analysis of Functional Genomic Data

描述（由申请人提供）：随着高通量分子测定技术的出现，生物学家不得不处理高维基因组数据集的分析。虽然已经提出了统计方法的问题，如差异表达与这些数据，相对较少的工作已经做了在人类疾病的高通量生物数据的统计分析方面，将生物学知识。 在这项资助中，我们提出了复杂的高维生物数据建模的统计程序的发展，重点是将功能生物学知识。 我们提出的方法将在生物学家可用的软件中实现和分发。虽然该资助中的主要生物学数据示例来自癌症的微阵列实验，但这里提出的方法是通用的，可以开发用于研究其他背景下的高维基因型-表型关联。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.基于基因表达和通路数据的整合，开发用于评估功能通路作用的统计方法。 4.基于基因表达和染色体定位数据的整合，开发用于确定过表达和低表达区域的统计方法。 5.用方便用户的统计软件传播这些结果。

DESCRIPTION (provided by applicant): 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 high-dimensional 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.