标题： 使用非局部分布混合物的多重假设筛查及其在基因组研究中的应用 显示英文标题

标题： Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies

摘要： 大规模数据集的分析，尤其是在生物医学背景下，通常涉及多个假设的原则性筛选。著名的两组模型通过混合两种竞争密度（零假设和备择假设分布）共同对检验统计量的分布进行建模。 我们研究了使用加权密度（特别是非局部密度）作为工作备择分布的可能性，以强制区分零假设并因此改进筛选过程。我们展示了这些加权备择分布如何在固定混合比例的情况下，相对于局部无权重似然方法，改善各种操作特性（例如贝叶斯错误发现率）。 提出了参数化和非参数化的模型规范，并提供了高效的后验推理抽样器。通过模拟研究，我们展示了我们的模型与已建立的以及最先进的替代方法在各种操作特性方面的比较情况。 最后，为了展示我们方法的多功能性，我们使用来自基因组学异质研究的公开数据集进行了三项差异表达分析。 显示英文摘要

摘要： The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian False Discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature.