León-Novelo Luis G, Müller Peter, Arap Wadih, Kolonin Mikhail, Sun Jessica, Pasqualini Renata, Do Kim-Anh
Department of Mathematics, University of Louisiana at Lafayette, Lafayette, Louisiana 70504-1010, USA.
Biometrics. 2013 Mar;69(1):174-83. doi: 10.1111/j.1541-0420.2012.01817.x. Epub 2013 Jan 22.
We discuss inference for a human phage display experiment with three stages. The data are tripeptide counts by tissue and stage. The primary aim of the experiment is to identify ligands that bind with high affinity to a given tissue. We formalize the research question as inference about the monotonicity of mean counts over stages. The inference goal is then to identify a list of peptide-tissue pairs with significant increase over stages. We use a semiparametric Dirichlet process mixture of Poisson model. The posterior distribution under this model allows the desired inference about the monotonicity of mean counts. However, the desired inference summary as a list of peptide-tissue pairs with significant increase involves a massive multiplicity problem. We consider two alternative approaches to address this multiplicity issue. First we propose an approach based on the control of the posterior expected false discovery rate. We notice that the implied solution ignores the relative size of the increase. This motivates a second approach based on a utility function that includes explicit weights for the size of the increase.
我们讨论了一个分三个阶段进行的人类噬菌体展示实验的推断问题。数据是按组织和阶段分类的三肽计数。该实验的主要目的是识别与特定组织具有高亲和力结合的配体。我们将研究问题形式化为关于各阶段平均计数单调性的推断。推断目标则是识别出在各阶段有显著增加的肽 - 组织对列表。我们使用泊松模型的半参数狄利克雷过程混合模型。在此模型下的后验分布允许对平均计数的单调性进行所需的推断。然而，作为有显著增加的肽 - 组织对列表的所需推断总结涉及大量的多重性问题。我们考虑两种替代方法来解决这个多重性问题。首先，我们提出一种基于控制后验预期错误发现率的方法。我们注意到隐含的解决方案忽略了增加的相对大小。这促使我们提出第二种基于效用函数的方法，该效用函数为增加的大小包含明确的权重。
