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

Robust network-based analysis of the associations between (epi)genetic measurements

With its important biological implications, modeling the associations of gene expression (GE) and copy number variation (CNV) has been extensively conducted. Such analysis is challenging because of the high data dimensionality, lack of knowledge regulating CNVs for a specific GE, different behaviors of the cis-acting and trans-acting CNVs, possible long-tailed distributions and contamination of GE measurements, and correlations between CNVs. The existing methods fail to address one or more of these challenges. In this study, a new method is developed to model more effectively the GE-CNV associations. Specifically, for each GE, a partially linear model, with a nonlinear cis-acting CNV effect, is assumed. A robust loss function is adopted to accommodate long-tailed distributions and data contamination. We adopt penalization to accommodate the high dimensionality and identify relevant CNVs. A network structure is introduced to accommodate the correlations among CNVs. The proposed method comprehensively accommodates multiple challenging characteristics of GE-CNV modeling and effectively overcomes the limitations of existing methods. We develop an effective computational algorithm and rigorously establish the consistency properties. Simulation shows the superiority of the proposed method over alternatives. The TCGA (The Cancer Genome Atlas) data on the PCD (programmed cell death) pathway are analyzed, and the proposed method has improved prediction and stability and biologically plausible findings.