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Cancer Inform. 2015 Nov 15;13(Suppl 7):123-31. doi: 10.4137/CIN.S16353. eCollection 2015.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data.

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Department of Statistics, University of Wisconsin, Madison, WI, USA.
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
Department of Statistics, University of Wisconsin, Madison, WI, USA. ; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.


In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies.


boosting; breast cancer; integrative genomic analysis; multivariate regression

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