Inferring gene regulatory relationships with a high ‐dimensional robust approach

In this study, we develop a high‐dimensional robust regression approach to infer the regulatory relationships between GEs and CNAs. A high‐dimensional regression model is used to accommodate the effects of both cis‐acting and trans‐acting CNAs. A density power divergence loss function is used to accommodate long‐tailed GE distributions and contamination. Penalization is adopted for regularized estimation and selection of relevant CNAs. The proposed approach is effectively realized using a coordinate descent algorithm. Simulation shows that it has competitive performance compared to the nonrobust benchmark and the robust LAD (least absolute deviation) approach. We analyze TCGA (The Cancer Genome Atlas) data on cutaneous melanoma and study GE‐CNA regulations in the RAP (regulation of apoptosis) pathway, which further demonstrates the satisfactory performance of the proposed approach.

Source: Genetic Epidemiology - May 2, 2017 Category: Epidemiology Authors: Yangguang Zang, Qing Zhao, Qingzhao Zhang, Yang Li, Sanguo Zhang, Shuangge Ma Tags: RESEARCH ARTICLE Source Type: research