Integrative methods for high-dimensional genomics data

描述(由申请人提供)：这项建议的主要目标是开发自适应和灵活的统计模型，用于分析来自高通量生物医学研究的多变量、功能和空间数据。这些研究提出了关于高维以及由生成数据的过程的各个方面引起的结构化依赖的计算、建模和推理方面的挑战。我们的工作是由我们的生物医学合作者在基因组学、表观基因组学和转录组学领域进行的各种高通量癌症相关研究的数据推动的，并将应用于这些数据；尽管我们的方法通常适用于其他情况。这项研究的短期目标是开发新的统计方法和计算工具，用于对这种高通量数据进行统计和概率建模，特别强调综合方法，以组合不同分析和临床数据中的信息，以回答重要的生物学问题。我们的长期目标是改进癌症预防、诊断和预后方面的风险预测和治疗选择。我们将通过追求以下特定目标来实现本应用的目标：(1)开发贝叶斯自适应广义泛函线性混合模型的新方法，允许标量响应和函数预测之间的局部和非线性关联结构(2)开发用于集成不同类型的多变量和函数数据的分层贝叶斯联合模型。(3)为空间索引的高维函数数据开发贝叶斯空间-函数过程模型，该方法用于需要更广泛类型的函数内和函数间协方差结构的数据，使用可伸缩的协方差函数族。(4)建立用于多个空间索引函数数据联合建模的多变量贝叶斯空间函数模型。(5)为所提出的方法开发专门的、用户友好的和免费的软件。

DESCRIPTION (provided by applicant): The primary objective of this proposal is to develop adaptive and exible statistical models for analyses of multivariate, functional and spatial data from high-throughput biomedical studies. These studies raise computational, modeling, and inferential challenges with respect to high-dimensionality as well as structured dependency induced by the various aspects of the processes generating the data. Our work is motivated by, and will be applied to, data from a variety of high- throughput cancer-related studies that were conducted by our biomedical collaborators, in genomics, epigenomics and transcriptomics; although our methods are generally applicable to other contexts. The short-term objective of this research is to develop novel statistical methods and computational tools for statistical and probabilistic modeling of such high-throughput data with particular emphasis on integrative methods to combine information within and across dierent assays as well as clinical data to answer important biological questions. Our long-term goal is to improve risk prediction and treatment selection in cancer prevention, diagnosis and prognosis. We will accomplish the objective of this application by pursuing the following ve specic aims (1) develop new methodology for Bayesian adaptive generalized functional linear mixed models, allowing for local and nonlinear association structures between scalar responses and functional predictors (2) develop hierarchical Bayesian joint models for integrating diverse types of multivariate and functional data. (3) develop Bayesian spatial-functional process models for spatially indexed high-dimensional functional data, methods for data requiring a broader class of within-function and between-function covariance structures using exible families of covariance functions. (4) develop multivariate Bayesian spatial-functional models for joint modeling of multiple spatially indexed functional data. (5) develop ecient, user-friendly and freely available software for the proposed methods.