Linearly Constrained Lasso with Application in Glioblastoma Data

• Activity Number: 114
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Peng Zeng*+ and Yu Zhu and Tianhong He
• Companies: Auburn University and Purdue University and Twitter
• Keywords: lasso ; variable selection ; solution path

Abstract

The knowledge and information on cancer is continuously accumulated as the advances in cancer research. How to appropriately incorporate them in data analysis to obtain more meaningful results presents a challenge to the statistical society. In this talk, we are concentrated in Glioblastoma, a most common and aggressive brain cancer. The objective is to identify genes that are related to Glioblastoma with incorporating the information on genetic pathways. The problem is formulated as a linearly constrained lasso problem. In general we have a lasso-type problem with linear equality and inequality constraints. We develop a solution path algorithm to fit this model efficiently, and also work out some asymptotic properties to understand its advantages. The method is proven to be efficient and flexible as demonstrated in simulation studies and real data analysis. This is a joint work with Yu Zhu and Tianhong He.