Concordant Integrative Analysis of Multiple Gene Expression Data Sets

描述（由申请人提供）：我们的长期目标是开发有效的统计和计算方法，对当前丰富的大规模基因组学，蛋白质组学和转录组学数据进行不同类型的综合分析，并进一步了解复杂疾病的病因，并改善其预防和治疗。在本项目中，重点将是对多个大规模基因表达数据集进行协调整合分析。在多个相关研究中表现出一致行为的基因和基因集具有重要的生物学意义。然而，缺乏有效的统计方法来进行这类分析。本项目提出以下具体目标：(1)开发从多个大规模基因表达数据集中检测一致性差异表达的统计方法；(2)开发从多个大规模基因表达数据集中检测一致性基因集富集的统计方法；(3)开发用户友好的基于r包的软件。在这个提议的项目中要开发的所有统计方法都将在免费的r包中实现。我们的方法和软件也将有助于其他类似的大规模数据集的协调综合分析。模拟数据和实验数据将用于严格评估我们的方法的性能。我们的方法将特别适用于为衰老和癌症研究收集的基因表达数据集。

DESCRIPTION (provided by applicant): Our long-term goals are to develop efficient statistical and computational methods for different types of integrative analysis of the current wealthy collection of large-scale genomics, proteomics and transcriptomics data, and to further understand the etiology of complex diseases as well as to improve their preventions and treatments. In this proposed project, the focus will be the concordant integrative analysis of multiple large- scale gene expression data sets. Genes and gene sets showing consistent behavior in multiple related studies are of great biological interest. However, there is a lack of efficient statistical methods for this type of analysis. The following specific aims are proposed for this project: (1) To develop statistical methods for the detection of concordant differential expression from multiple large-scale gene expression data sets; (2) To develop statistical methods for the detection of concordant gene set enrichment from multiple large-scale gene expression data sets; (3) To develop user-friendly R-package based software. All the statistical methods to be developed in this proposed project will be implemented in the free R-package. Our methods and software will also be useful for a concordant integrative analysis of other similar large-scale data sets. Simulated data as well as experimental data will be used to rigorously evaluate the performance of our methods. Our methods will be particularly applied to the gene expression data sets collected for aging and cancer studies. PUBLIC HEALTH RELEVANCE: Efficient statistical and computational methods will be developed for different types of concordant integrative analysis of large-scale gene expression data. The methods will be applied to the gene expression data sets for aging and cancer studies. R-package based computer software will be developed, documented and freely distributed to the scientific community.