Concordant Integrative Analysis of Multiple Gene Expression Data Sets

多基因表达数据集的一致综合分析

• 批准号: 7993295
• 负责人: YINGLEI LAI
• 金额: $ 15.07万
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7993295
• 关键词: Address; Aging; Behavior; Biological; Biological Markers; Collection; Communities; Complex; Computer software; Computing Methodologies; Data; Data Set; Detection; Disease; Etiology; Gene Expression; Genes; Genomics; Goals; Malignant Neoplasms; Meta-Analysis; Methods; Modeling; Molecular Profiling; Pathway interactions; Performance; Probability; Proteomics; Sample Size; Sampling; Simulate; Solutions; Statistical Methods; Technology; base; genome-wide; improved; interest; public health relevance; transcriptomics; user-friendly

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.

描述(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以及改进其预防和治疗。在这个拟议的项目中，重点将是对多个大规模基因表达数据集的整合分析。在多个相关研究中表现出一致行为的基因和基因集具有很大的生物学意义。然而，对于这种类型的分析，缺乏有效的统计方法。本项目的具体目标是：(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丰富的统计方法；(3)开发基于用户友好的R-Package软件。将在这一拟议项目中开发的所有统计方法都将在免费的R包中实施。我们的方法和软件也将有助于对其他类似的大规模数据集进行协调的综合分析。模拟数据和实验数据将被用来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。 公共卫生相关性：将为大规模基因表达数据的不同类型的协调综合分析开发有效的统计和计算方法。这些方法将应用于衰老和癌症研究的基因表达数据集。将开发和记录基于R-Package的计算机软件并免费分发给科学界。