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arXiv · 2011.00442

Penalized estimation for single-index varying-coefficient models with applications to integrative genomic analysis

Hoi Min Ng·Binyan Jiang·Kin Yau Wong
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Abstract

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.

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Hoi Min Ng, Binyan Jiang, Kin Yau Wong. 2020-11-01. Penalized estimation for single-index varying-coefficient models with applications to integrative genomic analysis. https://arxiv.org/abs/2011.00442

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