Statistical Methods for High-Throughput Gene Expression Profiling

高通量基因表达谱的统计方法

• 批准号: 8580300
• 负责人: JOHN D STOREY
• 金额: $ 37.79万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-19 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8580300
• 关键词: Animal Model; Bioinformatics; Biological; Biology; Communities; Complex; Computer software; Data; Data Set; Dependence; Dissection; Evaluation; Gene Expression; Gene Expression Profiling; Genes; Genome; Genomics; Goals; Human; Language; Link; Measurement; Measures; Medicine; Methods; Modeling; Molecular Profiling; Phenotype; Play; Programming Languages; Research; Research Design; Role; Sample Size; Simulate; Source; Source Code; Statistical Methods; Statistical Models; System; Testing; Update; Validation; Variant; Work; Writing; base; design; flexibility; genome-wide; improved; new technology; next generation; next generation sequencing; novel; novel strategies; platform-independent; programs; public health relevance; sound; tool; transcriptome sequencing; user-friendly; web site

DESCRIPTION (provided by applicant): Studies carried out at the genome-wide level now play a central role in modern biology and medicine. There continues to be a substantial need for new statistical methods that can be applied in these studies, particularly as study designs become more ambitious, sample sizes increase, and new technologies emerge. The overall goal of the proposed research is to develop statistical methods and software useful in understanding high- throughput molecular profiling data centered around characterizing genome-wide gene expression. We propose to develop statistical models, methods, and software that allow one to rigorously characterize variation of gene expression in terms of both study design and latent variables. Our proposed research is particularly focused on the most modern form of gene expression profiling, RNA-Seq, as well as the most ambitious and biologically fruitful problems currently being studied. We will develop rigorous, flexible, and robust models of variation in high-throughput data that encompass: (i) latent sources of systematic variation and more general sources of dependence among features, (ii) a principled dissection of sources of variation in next-generation RNA-Seq data and new methods for emerging RNA-Seq data, (iii) rigorous multiple hypothesis testing of associations between genomics features and latent variables, (iv) simultaneous inference of complex, yet commonly sought after statistical hypotheses, and (v) dissemination to the greater research community through user-friendly and platform independent software packages.

描述（由申请人提供）：在全基因组水平上进行的研究现在在现代生物学和医学中起着核心作用。这些研究仍然需要新的统计方法，特别是随着研究设计变得更加雄心勃勃，样本量增加和新技术的出现。提出的研究的总体目标是开发统计方法和软件，用于理解以全基因组基因表达为中心的高通量分子分析数据。我们建议开发统计模型、方法和软件，允许人们在研究设计和潜在变量方面严格表征基因表达的变化。我们建议的研究特别集中在最现代形式的基因表达谱，RNA-Seq，以及目前正在研究的最雄心勃勃和生物学上富有成果的问题。我们将开发严格、灵活和稳健的高通量数据变化模型，包括：(i)系统变异的潜在来源和特征之间更一般的依赖来源，（ii）对下一代RNA-Seq数据的变异来源和新兴RNA-Seq数据的新方法进行有原则的剖析，（iii）对基因组学特征和潜在变量之间的关联进行严格的多重假设检验，（iv）同时推断复杂的，但通常寻求的统计假设，(v)通过用户友好和平台独立的软件包向更大的研究界传播。