Incorporating Chromosomal Spatial Correlation Through 3D Structures Into Microarray Data Analysis

• Activity Number: 189
• Type: Invited
• Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Xinlei Wang*+ and Guanghua Xiao and Arkady Khodursky
• Companies: Southern Methodist University and UT Southwestern Medical Center and University of Minnesota
• Keywords: spatial modeling ; helical structures ; hierarchical Bayes ; MCMC

Abstract

Recent genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Interestingly, co-expression has been observed among genes separated by a fixed interval in specific regions of a chromosome chain, which is likely caused by three-dimensional (3D) chromosome folding structures. Ignoring such correlation in statistical modeling can reduce the efficiency of estimation and the power of statistical inference. Further, modeling the spatial correlation explicitly leads to essential understandings of 3D chromosome structures and their roles in transcriptional regulation. In this study, we proposed a hierarchical Bayesian method to formally model and incorporate the correlation into the analysis of gene expression microarray data. It is the first study to quantify and infer 3D chromosome structures in vivo using expression microarray.