Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models.
Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models.
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DOI：
10.1214/09-ba426
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发表时间：
2009
影响因子：
4.4
通讯作者：
Vannucci M
中科院分区：
文献类型：
--
作者：
Kim S;Dahl DB;Vannucci M
我们提出了一种用于随机效应模型中多重假设检验的贝叶斯方法，该方法使用狄利克雷过程（DP）先验对随机效应分布进行非参数处理。我们考虑一个适应各种多种治疗条件的通用模型公式。我们方法的一个关键特征是使用尖峰分布的乘积，即点质量和连续分布的混合，作为 DP 先验的中心分布。采用这些尖峰居中先验很容易适应尖锐的零假设，并允许估计此类假设的后验概率。狄利克雷过程混合模型通过基于模型的聚类自然地借用跨对象的信息，同时推断聚类不确定性的单一假设平均值。我们通过模拟研究证明，与其他竞争方法相比，我们的方法在多重假设检验中具有更高的灵敏度，并且产生的错误发现比例更低。虽然我们的建模框架是通用的，但在这里我们提出了在微阵列实验的基因表达背景下的应用。在我们的应用程序中，建模框架允许同时推断控制差异表达的参数和推断基因的聚类。我们使用小鼠心肌氧化应激转录反应的实验数据，并将我们的程序的结果与现有的非参数贝叶斯方法进行比较，现有的非参数贝叶斯方法仅根据差异表达的证据提供基因的排名。
We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditions. A key feature of our method is the use of a product of spiked distributions, i.e., mixtures of a point-mass and continuous distributions, as the centering distribution for the DP prior. Adopting these spiked centering priors readily accommodates sharp null hypotheses and allows for the estimation of the posterior probabilities of such hypotheses. Dirichlet process mixture models naturally borrow information across objects through model-based clustering while inference on single hypotheses averages over clustering uncertainty. We demonstrate via a simulation study that our method yields increased sensitivity in multiple hypothesis testing and produces a lower proportion of false discoveries than other competitive methods. While our modeling framework is general, here we present an application in the context of gene expression from microarray experiments. In our application, the modeling framework allows simultaneous inference on the parameters governing differential expression and inference on the clustering of genes. We use experimental data on the transcriptional response to oxidative stress in mouse heart muscle and compare the results from our procedure with existing nonparametric Bayesian methods that provide only a ranking of the genes by their evidence for differential expression.