Open AccessRegularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer.
Author(s) -
Jie Peng,
Ji Zhu,
Anna Bergamaschi,
Wonshik Han,
Dong Young Noh,
Jonathan R. Pollack,
Pei Wang
Publication year - 2010
Publication title -
pubmed
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.674
H-Index - 75
pISSN - 1932-6157
DOI - 10.1214/09-aoas271supp
Subject(s) - multivariate statistics , computational biology , breast cancer , regression , curse of dimensionality , multivariate analysis , regression analysis , bayesian multivariate linear regression , genomics , correlation , biology , computer science , oncology , cancer , statistics , mathematics , artificial intelligence , gene , genome , genetics , machine learning , medicine , geometry
In this paper, we propose a new method remMap - REgularized Multivariate regression for identifying MAster Predictors - for fitting multivariate response regression models under the high-dimension-low-sample-size setting. remMap is motivated by investigating the regulatory relationships among different biological molecules based on multiple types of high dimensional genomic data. Particularly, we are interested in studying the influence of DNA copy number alterations on RNA transcript levels. For this purpose, we model the dependence of the RNA expression levels on DNA copy numbers through multivariate linear regressions and utilize proper regularization to deal with the high dimensionality as well as to incorporate desired network structures. Criteria for selecting the tuning parameters are also discussed. The performance of the proposed method is illustrated through extensive simulation studies. Finally, remMap is applied to a breast cancer study, in which genome wide RNA transcript levels and DNA copy numbers were measured for 172 tumor samples. We identify a trans-hub region in cytoband 17q12-q21, whose amplification influences the RNA expression levels of more than 30 unlinked genes. These findings may lead to a better understanding of breast cancer pathology.