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。对可应用于这些研究的新统计方法的需求仍然很大，特别是在研究设计变得更加雄心勃勃、样本量增加和新技术出现的情况下。这项拟议研究的总体目标是开发统计方法和软件，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图谱数据。我们建议开发统计模型、方法和软件，使人们能够根据研究设计和潜在变量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的基因表达谱形式，RNA-Seq，以及目前正在研究的最雄心勃勃和生物学上最有成果的问题。我们将在高通量数据中开发严格、灵活和稳健的变异模型，其中包括：(I)系统变异的潜在来源和特征之间更普遍的依赖来源，(Ii)对下一代RNA-Seq数据中的变异源和新出现的RNA-Seq数据的新方法进行原则性的剖析，(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)同时推断复杂但普遍受到追捧的统计假设，以及(V)通过用户友好型和独立于平台的软件包向更广泛的研究社区传播。

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.