Publications
Detailed Information
REGULARIZED MULTIVARIATE REGRESSION FOR IDENTIFYING MASTER PREDICTORS WITH APPLICATION TO INTEGRATIVE GENOMICS STUDY OF BREAST CANCER
Cited 179 time in
Web of Science
Cited 113 time in Scopus
- Authors
- Issue Date
- 2010-03
- Publisher
- INST MATHEMATICAL STATISTICS
- Citation
- ANNALS OF APPLIED STATISTICS; Vol.4 1; 53-77
- Keywords
- Sparse regression ; MAP (MAster Predictor) penalty ; DNA copy number alteration ; v-fold cross validation ; RNA transcript level
- Abstract
- 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.
- ISSN
- 1932-6157
- Language
- English
- Files in This Item:
- There are no files associated with this item.
- Appears in Collections:
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.