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Activity Number:
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250
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #302907 |
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Title:
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Regularized Multivariate Regression for Identifying Master Predictors
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Author(s):
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Jie Peng*+ and Pei Wang and Ji Zhu and Jonathan Pollack
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Companies:
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University of California, Davis and Fred Hutchinson Cancer Research Center and University of Michigan and Stanford University
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Address:
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Department of Statistics, Davis, CA, 95616,
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Keywords:
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sparse regression ; MAster Predictor ; DNA copy number alteration ; RNA transcript level
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Abstract:
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We propose a new method remMap 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 regularizations to deal with the high dimensionality as well as to incorporate desired network structures. RemMap is applied to a breast cancer study, in which genome wide RNA transcript levels and DNA copy numbers were measured. We identify a trans-hub region whose amplification influences the RNA express of 30 unlinked genes.
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