This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Recently, many genomic studies have shown that significant chromosomal spatial correlation exists in gene expression of many organisms. Ignoring such correlation in statistical modeling can greatly reduce the efficiency of estimation and the power of statistical inference. In this project, the investigators develop a set of new Bayesian hierarchical models to account for chromosomal spatial correlation in the following three areas of methodologies and applications: (1) incorporating the spatial correlation associated with linear chromosome structures into the analysis of gene expression data; (2) quantifying and inferring chromosome folding structures in vivo using gene expression data; (3) probing the global three dimensional structure of a chromosome in vivo.
This study provides statistical tools to explore three dimensional (3D) chromosome structures and directly addresses the important biological question that how the 3D structures facilitate the coordination in gene transcription. Not only can it generate new biological insights about spatial configuration of chromosomes, but also it can greatly improve the understanding of the relationship between the function and structure of chromosomes in living organisms. It also has potential clinical significance; for example, it provides tools to formally identify and compare chromosomal spatial patterns in gene expression from tumor and normal samples, which can discover subtle but coordinated changes in the tumor genome and lead to clinical insights on the underlying regulatory mechanism of cancer. Besides scientific novelty and significance, this research fosters intensive collaboration among researchers from various fields including statistics, computational biology, experimental biology and clinical cancer research, and promotes intellectual interactions among participating institutions.
