Lewin Alex, Bochkina Natalia, Richardson Sylvia
Imperial, London.
Stat Appl Genet Mol Biol. 2007;6:Article36. doi: 10.2202/1544-6115.1314. Epub 2007 Dec 21.
We present a Bayesian hierarchical model for detecting differentially expressed genes using a mixture prior on the parameters representing differential effects. We formulate an easily interpretable 3-component mixture to classify genes as over-expressed, under-expressed and non-differentially expressed, and model gene variances as exchangeable to allow for variability between genes. We show how the proportion of differentially expressed genes, and the mixture parameters, can be estimated in a fully Bayesian way, extending previous approaches where this proportion was fixed and empirically estimated. Good estimates of the false discovery rates are also obtained. Different parametric families for the mixture components can lead to quite different classifications of genes for a given data set. Using Affymetrix data from a knock out and wildtype mice experiment, we show how predictive model checks can be used to guide the choice between possible mixture priors. These checks show that extending the mixture model to allow extra variability around zero instead of the usual point mass null fits the data better. A software package for R is available.
我们提出了一种贝叶斯层次模型，用于使用代表差异效应的参数上的混合先验来检测差异表达基因。我们构建了一个易于解释的三成分混合模型，将基因分类为过表达、低表达和非差异表达，并将基因方差建模为可交换的，以允许基因之间存在变异性。我们展示了如何以完全贝叶斯的方式估计差异表达基因的比例和混合参数，扩展了之前该比例固定且凭经验估计的方法。还获得了对错误发现率的良好估计。对于给定数据集，混合成分的不同参数族可能导致对基因的截然不同的分类。使用来自基因敲除和野生型小鼠实验的Affymetrix数据，我们展示了如何使用预测模型检查来指导在可能的混合先验之间进行选择。这些检查表明，扩展混合模型以允许围绕零有额外的变异性，而不是通常的点质量零假设，能更好地拟合数据。有一个用于R的软件包可用。
