REGULARIZED MULTIVARIATE REGRESSION FOR IDENTIFYING MASTER PREDICTORS WITH APPLICATION TO INTEGRATIVE GENOMICS STUDY OF BREAST CANCER
REGULARIZED MULTIVARIATE REGRESSION FOR IDENTIFYING MASTER PREDICTORS WITH APPLICATION TO INTEGRATIVE GENOMICS STUDY OF BREAST CANCER
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DOI：
10.1214/09-aoas271
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发表时间：
2010-03-01
影响因子：
1.8
通讯作者：
Wang, Pei
中科院分区：
文献类型：
--
作者：
Peng, Jie;Zhu, Ji;Wang, Pei
本文提出了一种新的方法remMap-正则化多元回归识别MAster预测-在高维低样本设置下拟合多元响应回归模型。remMap的动机是基于多种类型的高维基因组数据来研究不同生物分子之间的调控关系。特别是，我们有兴趣研究DNA拷贝数的改变对RNA转录水平的影响。为此，我们通过多元线性回归模型的RNA表达水平对DNA拷贝数的依赖性，并利用适当的正则化来处理高维以及纳入所需的网络结构。本文还讨论了调谐参数的选择准则。所提出的方法的性能说明通过广泛的仿真研究。最后，将remMap应用于乳腺癌研究，其中测量了172个肿瘤样本的全基因组RNA转录水平和DNA拷贝数。我们在细胞带17 q12-q21中确定了一个trans-hub区域，该区域的扩增影响了30多个非连锁基因的RNA表达水平。这些发现可能有助于更好地了解乳腺癌病理学。
In this paper we propose a new method remMap-REgularized Multivariate regression for identifying MAster Predictors-for fitting multivariate response regression models under the high-dimension-low-sample-size setting. remMap is motivated by investigating the regulatory relationships among different biological molecules based on multiple types of high dimensional genomic data. Particularly, we are interested in studying the influence of DNA copy number alterations on RNA transcript levels. For this purpose, we model the dependence of the RNA expression levels on DNA copy numbers through multivariate linear regressions and utilize proper regularization to deal with the high dimensionality as well as to incorporate desired network structures. Criteria for selecting the tuning parameters are also discussed. The performance of the proposed method is illustrated through extensive simulation studies. Finally, remMap is applied to a breast cancer study, in which genome wide RNA transcript levels and DNA copy numbers were measured for 172 tumor samples. We identify a trans-hub region in cytoband 17q12-q21, whose amplification influences the RNA expression levels of more than 30 unlinked genes. These findings may lead to a better understanding of breast cancer pathology.