Empirical Bayes ranking and selection methods via semiparametric hierarchical mixture models in microarray studies
Empirical Bayes ranking and selection methods via semiparametric hierarchical mixture models in microarray studies
复制标题
DOI：
10.1002/sim.5718
复制
发表时间：
2013-05
影响因子：
2
通讯作者：
H. Noma;S. Matsui
中科院分区：
文献类型：
--
作者：
H. Noma;S. Matsui
微阵列研究的主要目的是筛选差异表达的基因作为进一步研究的候选基因。由于这一阶段的资源有限，基因排序是微阵列研究中的相关统计任务。对于有效的基因选择，已经提出了用于排序和选择具有最大效应大小的基因的参数经验贝叶斯方法（Noma等人，2010; Biostatistics 11：281-289）。分层混合模型结合了差异和非差异成分，并允许跨差异基因的信息借用，并与滋扰、非差异基因分离。在这篇文章中，我们通过半参数分层混合模型开发经验贝叶斯排序方法。效应量的非参数先验分布，而不是参数先验分布，是使用Laird和Louis的“粗糙化平滑”方法指定和估计的（1991;计算统计和数据分析12：27-37）。我们目前的应用程序，儿童和婴儿白血病的临床研究与微阵列探索基因的预后或疾病进展。版权所有© 2012约翰威利父子有限公司.
The main purpose of microarray studies is screening of differentially expressed genes as candidates for further investigation. Because of limited resources in this stage, prioritizing genes are relevant statistical tasks in microarray studies. For effective gene selections, parametric empirical Bayes methods for ranking and selection of genes with largest effect sizes have been proposed (Noma et al., 2010; Biostatistics 11: 281–289). The hierarchical mixture model incorporates the differential and non‐differential components and allows information borrowing across differential genes with separation from nuisance, non‐differential genes. In this article, we develop empirical Bayes ranking methods via a semiparametric hierarchical mixture model. A nonparametric prior distribution, rather than parametric prior distributions, for effect sizes is specified and estimated using the “smoothing by roughening” approach of Laird and Louis (1991; Computational Statistics and Data Analysis 12: 27–37). We present applications to childhood and infant leukemia clinical studies with microarrays for exploring genes related to prognosis or disease progression. Copyright © 2012 John Wiley & Sons, Ltd.