Statistical Methods for High-Throughput Gene Expression Profiling

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发挥着核心作用。在这些研究中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。拟议研究的总体目标是开发有助于理解以表征全基因组基因表达为中心的高通量分子谱数据的统计方法和软件。我们建议开发的统计模型，方法和软件，使一个严格的特征基因表达的变化方面的研究设计和潜变量。我们提出的研究特别关注基因表达谱的最现代形式，RNA-Seq，以及目前正在研究的最雄心勃勃和生物学上最富有成效的问题。我们将开发严格，灵活和强大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间依赖性的更一般来源，（ii）下一代RNA-Seq数据中变异来源的原则性剖析和新兴RNA-Seq数据的新方法，（iii）基因组学特征和潜在变量之间关联的严格多重假设检验，（iv）同时推断复杂但普遍寻求的统计假设，以及（v）通过用户友好和平台独立的软件包向更广泛的研究界传播。

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.