Estimation and selection in high-dimensional genomic studies for developing molecular diagnostics
Estimation and selection in high-dimensional genomic studies for developing molecular diagnostics
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DOI：
10.1093/biostatistics/kxq057
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发表时间：
2011-04-01
期刊：
影响因子：
2.1
通讯作者：
Noma, Hisashi
中科院分区：
文献类型：
--
作者：
Matsui, Shigeyuki;Noma, Hisashi
在分子诊断学的发展中，高维基因组研究(如DNA微阵列研究)的主要目标是筛选与临床表型密切相关的基因，以显著提高诊断能力。因此，基本的统计任务是估计单个基因的关联强度或效应大小。我们发展了一种基于分层混合模型的经验贝叶斯估计方法，用于基于基因的统计量关于效应大小的估计，而与差异表达的方向无关。在许多基因组研究中，由于关于效应大小的分布形式的信息有限，所以指定了非参数先验。我们的方法提供了一些后验指标，为进一步研究选择候选基因提供了有用的信息。我们可以评估任何基因集的预测能力，可能是通过结合生物学因素选择的那些基因集。提供了对来自癌症临床研究的2个基因表达数据集的应用。
In the development of molecular diagnostics, the main objective in high-dimensional genomic studies such as DNA microarray studies is to screen out genes strongly associated with clinical phenotypes to significantly improve diagnostic capabilities. The basic statistical task is thus estimation of the strengths of association or effect sizes for individual genes. We develop an empirical Bayes estimation method based on hierarchical mixture models for a gene-based statistic regarding effect size, without respect to the direction of differential expressions. A nonparametric prior is specified because of limited information on the distributional form of effect size in many genomic studies. Our methods provide some posterior indices useful for selecting candidate genes for further studies. We can assess the predictive capability for any gene sets, possibly those selected via incorporation of biological considerations. Applications to 2 gene expression data sets from cancer clinical studies with microarrays are provided.