Bayesian hierarchical structured variable selection methods with application to molecular inversion probe studies in breast cancer
Bayesian hierarchical structured variable selection methods with application to molecular inversion probe studies in breast cancer
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贝叶斯分层结构变量选择方法在乳腺癌分子倒置探针研究中的应用
DOI：
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
K. Do
中科院分区：
文献类型：
--
作者：
Lin Zhang;V. Baladandayuthapani;B. Mallick;G. Manyam;P. Thompson;M. Bondy;K. Do
对自然界中复制染色体片段时可能发生的基因组学改变(称为拷贝数改变)的分析一直是识别癌症遗传标记的研究重点。最近采用的一种高通量技术是使用分子反转探针来测量探针拷贝数的变化。由此得到的数据包括高维拷贝数分布，可用于确定与患者预后相关研究中探头特异拷贝数的变化，以指导风险分层和未来治疗。我们提出了一种新的贝叶斯变量选择方法，即分层结构变量选择方法，它考虑了自然基因和基因内探针的结构，以确定与临床相关结果相关的重要基因和探针。我们提出了分组变量选择的层次结构变量选择模型，其中分组变量和组内变量的同时选择是感兴趣的。分层结构变量选择模型利用离散混合先验分布进行组选择，并利用特定于组的贝叶斯套索层次结构进行组内变量选择。我们提供了将贝叶斯融合套索方法用于组内选择的组内序列相关性的核算方法。通过仿真，我们证明了当存在自然分组结构时，我们的方法比其他方法具有更低的模型误差。我们将我们的方法应用于乳腺癌的分子反转探针研究，并表明它识别了与临床相关乳腺癌亚型显著相关的基因和探针。
The analysis of genomics alterations that may occur in nature when segments of chromosomes are copied (known as copy number alterations) has been a focus of research to identify genetic markers of cancer. One high throughput technique that has recently been adopted is the use of molecular inversion probes to measure probe copy number changes. The resulting data consist of high dimensional copy number profiles that can be used to ascertain probe‐specific copy number alterations in correlative studies with patient outcomes to guide risk stratification and future treatment. We propose a novel Bayesian variable selection method, the hierarchical structured variable selection method, which accounts for the natural gene and probe‐within‐gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the hierarchical structured variable selection model for grouped variable selection, where simultaneous selection of both groups and within‐group variables is of interest. The hierarchical structured variable selection model utilizes a discrete mixture prior distribution for group selection and group‐specific Bayesian lasso hierarchies for variable selection within groups. We provide methods for accounting for serial correlations within groups that incorporate Bayesian fused lasso methods for within‐group selection. Through simulations we establish that our method results in lower model errors than other methods when a natural grouping structure exists. We apply our method to a molecular inversion probe study of breast cancer and show that it identifies genes and probes that are significantly associated with clinically relevant subtypes of breast cancer.
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影响因子：
4.5
作者：
Huang J;Ma S;Li H;Zhang CH
通讯作者：
Zhang CH
影响因子：
3.3
作者：
Shizhong Xu
通讯作者：
Shizhong Xu
DOI：
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发表时间：
2006
期刊：
--
影响因子：
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作者：
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通讯作者：
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影响因子：
7
作者：
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通讯作者：
Wigler, M
影响因子：
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作者：
E. George;R. McCulloch
通讯作者：
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