Matrix variate modeling and analysis of large scale biological data with complex dependencies

具有复杂依赖性的大规模生物数据的矩阵变量建模和分析

• 批准号: 1316731
• 负责人: Kerby Shedden
• 金额: $ 22万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1316731&HistoricalAwards=false
• 关键词: Matrix; variate; modeling; analysis; large

Matrix-variate models provide a way to analyze complex multivariate datasets in which meaningful relationships may exist among both the variables and among the observed units. This is an extension of the more standard setting of multivariate statistical analysis, in which the variables are dependent, but the observations are viewed as being independent. The primary motivation for treating the data as a single high-dimensional sample is the potential for increased power when estimating parameters that are sensitive to relationships among the experimental units. Recent advances in high-dimensional non-asymptotic theory, convex analysis, and algorithms allow such models to be fit to the very large data sets that arise in critical scientific areas such as genomics and neuroscience. A major goal of this project is to assess the extent to which accounting for relationships among samples allows more accurate estimates to be made of relationships among variables. An example of how this might be applied is in the assessment of associations between rare genetic variants and a phenotype. The Sequential Kernel Association Test (SKAT) uses a kernel matrix which is essentially a covariance matrix of the genetic features. The investigators will evaluate the use of the covariance matrix obtained from the recently-proposed Gemini approach to matrix-variate analysis as a kernel for the SKAT procedure. Three scientific applications have been identified to which the Gemini framework may be usefully extended. In addition to conceptual and algorithmic challenges, these applications require the theory and methodology for the Gemini estimators to cover new settings, for example, due to SNP genotypes being highly non-Gaussian, and due to brain connectivity graphs changing over time. The investigators will adapt the baseline Gemini models and algorithms to these new settings, and study their statistical and computational properties.Recent technological breakthroughs in instrumentation allow large and detailed data sets describing living systems to be efficiently collected. Researchers in biology and health science have embraced these technologies, but have sometimes been frustrated by the fact that such data must be interpreted conservatively, to guard against making non-reproducible claims. Research progress would be accelerated if it were possible to analyze such data in a way that provides more statistical power, without expensive increases in the sample size. One path to doing this is to move beyond the paradigm of treating the observed units in a study (e.g. human research subjects or laboratory animals) independently. The investigator and his colleagues have developed a new type of statistical procedure that uses inferred relationships among the units of observation to more accurately estimate physical and biological unknowns. They plan to develop the theory behind their technique, to develop software for carrying out the analyses, and to work with scientists in cancer biology, genomics, and neuroscience to assess the potential for using this approach to obtain novel scientific insights. The project will focus on three scientific problems: identification of DNA lesions involved with cancer incidence and progression, identification of inherited genetic variants associated with human diseases, and characterization of neural connectivity changes induced by changes in consciousness state.

矩阵变量模型提供了一种分析复杂多变量数据集的方法，其中变量之间和观察单位之间可能存在有意义的关系。这是更标准的多元统计分析设置的扩展，在这种情况下，变量是相关的，但观测被视为独立的。将数据作为单一的高维样本处理的主要动机是，在估计对实验单位之间的关系敏感的参数时，可能会增加能力。高维非渐近理论、凸分析和算法的最新进展使这些模型能够适用于在基因组学和神经科学等关键科学领域出现的非常大的数据集。这个项目的一个主要目标是评估在多大程度上考虑样本之间的关系可以更准确地估计变量之间的关系。这可能如何应用的一个例子是在评估稀有基因变异和表型之间的关联时。序贯核关联检验(SKAT)使用的核矩阵本质上是遗传特征的协方差矩阵。研究人员将评估使用从最近提出的双子座方法获得的协方差矩阵进行矩阵变量分析，作为SKAT程序的核心。已经确定了三种科学应用，双子座框架可以有效地扩展到这些应用领域。除了概念和算法上的挑战，这些应用还需要双子座估计器的理论和方法来涵盖新的环境，例如，由于SNP基因型是高度非高斯的，以及由于大脑连接图随着时间的变化。研究人员将使基线双子座模型和算法适应这些新的环境，并研究它们的统计和计算特性。最近在仪器方面的技术突破使描述生命系统的大量和详细的数据集得以有效收集。生物学和健康科学的研究人员接受了这些技术，但有时会感到沮丧，因为这些数据必须被保守地解释，以防止做出不可重现的说法。如果有可能以一种提供更多统计能力的方式分析这些数据，而不会增加昂贵的样本量，研究进展将会加快。要做到这一点，一种方法是超越单独对待研究中观察到的单位(例如，人类研究对象或实验动物)的范式。这位研究人员和他的同事开发了一种新的统计程序，它使用观察单位之间的推断关系来更准确地估计物理和生物未知因素。他们计划开发他们技术背后的理论，开发进行分析的软件，并与癌症生物学、基因组学和神经科学的科学家合作，评估使用这种方法获得新的科学见解的潜力。该项目将集中在三个科学问题上：识别与癌症发生和进展有关的DNA损伤，识别与人类疾病相关的遗传变异，以及表征意识状态变化引起的神经连接变化。