论文标题
对单个指数的惩罚估计，用于综合基因组分析的应用变化模型
Penalized estimation for single-index varying-coefficient models with applications to integrative genomic analysis
论文作者
论文摘要
最近的技术进步使得可以收集高维基因组数据以及大量受试者的临床数据。在癌症等慢性疾病的研究中，将临床和基因组数据整合以建立对疾病机制的全面理解是引起极大的兴趣。尽管对综合分析进行了广泛的研究，但由于数据类型之间的数据和异质性高差异，临床和基因组变量之间的相互作用效应仍然是一个持续的挑战。在本文中，我们提出了一种综合方法，该方法使用单个指数变化的模型对相互作用进行建模，其中基因组特征的效果可以通过临床变量来修改。我们提出了一种惩罚方法，以单独选择主要和相互作用效果。我们通过广泛的模拟研究证明了所提出的方法的优势，并为激励癌症基因组研究提供了应用。
Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high-dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.
