Incorporating Network Structure in Integrative Analysis of Cancer Prognosis Data
时 间: 2013年3月22日周五下午14:00-15:00
报告人：Shuangge Ma (马双鸽)
Dr. Ma is an Associate Professor in the Division of Biostatistics, School of Public Health and Computational Biology and Bioinformatics in Biological and Biomedical Sciences in Yale University. He received his Ph.D. in Statistics from the University of Wisconsin, Madison in 2004. He is currently an elected member of ISI (since 2007). His research interests are bioinformatics,survival analysis, semi-parametric methods, cancer study, health economics.
Abstract: In high-throughput cancer genomic studies, markers identified from the analysis of single datasets may have unsatisfactory properties because of low sample sizes. Integrative analysis pools and analyzes raw data from multiple studies, and can effectively increase sample size and lead to improved marker identification results. In this study, we consider the integrative analysis of multiple high-throughput cancer prognosis studies. In the existing integrative analysis studies, the interplay among genes, which can be described using the network structure, has not been effectively accounted for. In network analysis, tightly-connected nodes (genes) are more likely to have related biological functions and similar regression coefficients. The goal of this study is to develop a new analysis approach that can incorporate the gene network structure in integrative analysis. To this end, we adopt AFT (accelerated failure time) models to describe survival. For marker selection, we propose a new penalization approach. A group coordinate descent approach is developed to compute the proposed estimate. We analyze three lung cancer prognosis datasets, and demonstrate that incorporating the network structure can lead to the identification of important genes and improved prediction performance.