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

Signal extraction and breakpoint identification for array CGH data using robust state space model