  Array comparative genomic hybridization(CGH) is a high resolution technique
to assess DNA copy number variation. Identifying breakpoints where copy number
changes will enhance the understanding of the pathogenesis of human diseases,
such as cancers. However, the biological variation and experimental errors
contained in array CGH data may lead to false positive identification of
breakpoints. We propose a robust state space model for array CGH data analysis.
The model consists of two equations: an observation equation and a state
equation, in which both the measurement error and evolution error are specified
to follow t-distributions with small degrees of freedom. The completely
unspecified CGH profiles are estimated by a Markov Chain Monte Carlo(MCMC)
algorithm. Breakpoints and outliers are identified by a novel backward
selection procedure based on posterior draws of the CGH profiles. Compared to
three other popular methods, our method demonstrates several desired features,
including false positive rate control, robustness against outliers, and
superior power of breakpoint detection. All these properties are illustrated
using simulated and real datasets.
