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

TL;DR

This paper introduces a penalized single-index varying-coefficient model for integrating high-dimensional genomic and clinical data, effectively capturing interaction effects in cancer studies.

Contribution

It develops a novel penalized estimation method for modeling and selecting main and interaction effects in high-dimensional integrative genomic analysis.

Findings

Effective in simulation studies

Successfully applied to cancer genomic data

Improves understanding of disease mechanisms

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.