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Recent technological advances have made it possible to collecthigh-dimensional genomic data along with clinical data on a large number ofsubjects. In the studies of chronic diseases such as cancer, it is of greatinterest to integrate clinical and genomic data to build a comprehensiveunderstanding of the disease mechanisms. Despite extensive studies onintegrative analysis, it remains an ongoing challenge to model the interactioneffects between clinical and genomic variables, due to high-dimensionality ofthe data and heterogeneity across data types. In this paper, we propose anintegrative approach that models interaction effects using a single-indexvarying-coefficient model, where the effects of genomic features can bemodified by clinical variables. We propose a penalized approach for separateselection of main and interaction effects. We demonstrate the advantages of theproposed methods through extensive simulation studies and provide applicationsto a motivating cancer genomic study.

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