A Bayesian Data Integration Approach for Detecting Periodicity in Cell Cycle Gene Expression Profiles
Date: 21 January 2009 (Wednesday)
Time:12:30pm - 2:00pm
Venue: 126, C N Yang Reading Room, Science Centre North Block
Speaker: Dr. FAN Xiaodan, Department of Statistics, The Chinese University of Hong Kong
Abstract: There is a growing interest in statistical methods for integrating multiple sources of information in an effort to improve statistical inference and gain deeper understanding of biological systems. In this talk we present a Bayesian meta-analysis approach for integrating multiple microarray time-series data sets to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis-Hastings group move. A surprising finding from our integrated analysis is that about 40% or more of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported percentage of 10-15% in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem. It also shows that the power and potential of model-based data integration is appealing.
