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In this study, we propose a hidden Markov mixture model for the analysis ofgene expression measurements mapped to chromosome locations. These expressionvalues represent preprocessed light intensities observed in each probe ofAffymetrix oligonucleotide arrays. Here, the algorithm BLAT is used to alignthousands of probe sequences to each chromosome. The main goal is to identifygenome regions associated with high expression values which define clusterscomposed by consecutive observations. The proposed model assumes a mixturedistribution in which one of the components (the one with the highest expectedvalue) is supposed to accommodate the overexpressed clusters. The model takesadvantage of the serial structure of the data and uses the distance informationbetween neighbours to infer about the existence of a Markov dependence. Thisdependence is crucially important in the detection of overexpressed regions. Wepropose and discuss a Markov chain Monte Carlo algorithm to fit the model.Finally, the proposed methodology is used to analyse five data setsrepresenting three types of cancer (breast, ovarian and brain).

A Bayesian hidden Markov mixture model to detect overexpressed chromosome regions