小鼠和人：心血管基因组学中的稀疏统计建模
Of mice and men: Sparse statistical modeling in cardiovascular genomics
摘要：
在高通量基因组学中，大规模设计实验变得越来越普遍，基于高度多元回归和方差分析概念的分析方法是关键工具。一种或另一种形式的收缩模型可以提供综合的方法来解决同时推理的问题，同时推理涉及对许多许多参数的隐式多重比较，这些参数表示设计因素和协变量的影响。我们在心血管基因组学的研究中使用这种方法。主要的实验背景涉及一个精心设计的，丰富的，基因表达研究的重点是基因与环境的相互作用，识别与疾病状态和已知的风险因素有关的基因，并在生成表达签名作为这些风险因素的代理的目标。一个耦合的探索性分析研究了基因表达特征的跨物种外推-这些小鼠模型特征如何翻译到人类。后者涉及探索人类观测数据的稀疏潜在因素分析，以及它如何与动物模型中得出的预测风险特征相关。该研究还强调了一系列应用统计和基因组数据分析问题，包括模型规范，计算问题和基于模型的校正DNA微阵列数据中的实验文物。
英文摘要：
In high-throughput genomics, large-scale designed experiments are becoming common, and analysis approaches based on highly multivariate regression and anova concepts are key tools. Shrinkage models of one form or another can provide comprehensive approaches to the problems of simultaneous inference that involve implicit multiple comparisons over the many, many parameters representing effects of design factors and covariates. We use such approaches here in a study of cardiovascular genomics. The primary experimental context concerns a carefully designed, and rich, gene expression study focused on gene-environment interactions, with the goals of identifying genes implicated in connection with disease states and known risk factors, and in generating expression signatures as proxies for such risk factors. A coupled exploratory analysis investigates cross-species extrapolation of gene expression signatures--how these mouse-model signatures translate to humans. The latter involves exploration of sparse latent factor analysis of human observational data and of how it relates to projected risk signatures derived in the animal models. The study also highlights a range of applied statistical and genomic data analysis issues, including model specification, computational questions and model-based correction of experimental artifacts in DNA microarray data.
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