用于观察性基因表达研究的贝叶斯模型平均法
A Bayesian model averaging approach for observational gene expression studies
摘要：
鉴定与样品特征相关的差异表达（DE）基因是许多微阵列研究的主要目标。随着越来越多的研究采用观察性样本而非良好对照的实验样本进行，评估和适当控制样本异质性对DE基因发现的影响变得非常重要。用于鉴定DE基因的典型方法需要根据基于用于两个或更多个组比较的单个模型的预先选择的统计量对所有基因进行排序，其中调整或不调整其它协变量。这种单一模型方法必然导致模型误指定，这可能导致由于某些基因的偏差而增加的误差和其他基因的效率降低。我们评估了这种方法对检测DE基因的模型错误指定的影响，并确定了影响影响程度的参数。为了正确控制样本异质性，并提供一个灵活和连贯的框架，同时确定DE基因与一个或多个样本的特征和/或它们的相互作用，我们提出了一个贝叶斯模型平均的方法，纠正了模型的错误指定的平均模型空间形成的所有相关的协变量。一个经验的方法，建议指定先验模型的概率。我们通过模拟的微阵列数据表明，与单一模型方法相比，这种方法在DE基因识别中的性能得到了改善。这种方法的灵活性通过我们对两项观察性微阵列研究的数据分析得到了证明。
英文摘要：
Identifying differentially expressed (DE) genes associated with a sample characteristic is the primary objective of many microarray studies. As more and more studies are carried out with observational rather than well controlled experimental samples, it becomes important to evaluate and properly control the impact of sample heterogeneity on DE gene finding. Typical methods for identifying DE genes require ranking all the genes according to a preselected statistic based on a single model for two or more group comparisons, with or without adjustment for other covariates. Such single model approaches unavoidably result in model misspecification, which can lead to increased error due to bias for some genes and reduced efficiency for the others. We evaluated the impact of model misspecification from such approaches on detecting DE genes and identified parameters that affect the magnitude of impact. To properly control for sample heterogeneity and to provide a flexible and coherent framework for identifying simultaneously DE genes associated with a single or multiple sample characteristics and/or their interactions, we proposed a Bayesian model averaging approach which corrects the model misspecification by averaging over model space formed by all relevant covariates. An empirical approach is suggested for specifying prior model probabilities. We demonstrated through simulated microarray data that this approach resulted in improved performance in DE gene identification compared to the single model approaches. The flexibility of this approach is demonstrated through our analysis of data from two observational microarray studies.
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