This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 532
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #308559
Title: Block-Regulized Regression for Identifying CNV in Gene Expression Data
Author(s): Yanming Li*+
Companies: University of Michigan
Address: Dept. of Biostatistics, School of Public Health, Ann Arbor, MI, 48109,
Keywords: DNA Copy Number Variation ; Gene Expression Data ; Lasso ; Sparse Regression
Abstract:

DNA copy number variation (CNV) is a major cause of chromosomal disorder and it plays an important role in development of some diseases such as cancer and mental illnesses through expression levels of disease-related genes. Studies on relationship between gene expression levels and CNV are always carried out through multivariate linear regression models under high-dimensional setting in terms of large p small n scenario. For the case interested here, we model the diseases related gene-expression level on CNV through multivariate linear regression on human genome and utilize proper regularization to deal with high dimensionality and collinearity as well as to incorporate the prior network and group structures on both response and predictor variables. Our algorithm is compared with some other contemporary regulation methods in high-dimensional cases through some simulation studies.


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