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摘要翻译：
在高通量基因组学中，大规模设计性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。一种形式或另一种形式的收缩模型可以提供全面的方法来解决同时推断的问题，这些问题涉及对表示设计因素和协变量影响的许多许多参数的隐式多重比较。我们在心血管基因组学研究中使用了这样的方法。主要的实验内容涉及一个精心设计和丰富的基因表达研究，侧重于基因与环境的相互作用，其目标是识别与疾病状态和已知风险因素有关的基因，并生成表达特征作为这些风险因素的代理。一项耦合的探索性分析研究了基因表达特征的跨物种外推--这些小鼠模型特征是如何翻译到人类的。后者包括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联。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DNA微阵列数据实验伪影校正。
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英文标题：
《Of mice and men: Sparse statistical modeling in cardiovascular genomics》
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作者：
David M. Seo, Pascal J. Goldschmidt-Clermont, Mike West
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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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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PDF链接：
https://arxiv.org/pdf/709.0165
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