Title: Hierarchical models approach for detecting gene copy number changes

Abstract: Array comparative genomic hybridization (CGH) is a
technology used to detect DNA copy number alterations which help
identify the relevant genes for cancer development. This recent
technology development calls for new statistical methods for analyzing
array CGH data.  Here we propose a Bayesian method to analyze multiple
samples at the same time. Our method is based on hierarchical model
with samples grouped according to the disease progression and survival
status. The posterior probabilities of copy gain/loss are estimated
for each gene at the group level. From that result, we can also
classify new patients and identify the genes relevant to the group
difference. We will demonstrate our method on simulated and real data
sets.