Biostatistics Advance Access originally published online on July 27, 2005
Biostatistics 2006 7(1):85-99; doi:10.1093/biostatistics/kxi042
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Probabilistic segmentation and intensity estimation for microarray images
Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA raph{at}stat.washington.edu
* To whom correspondence should be addressed.
We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for complementary DNA microarray experiments. The approach overcomes several limitations of existing methods. In particular, it (a) uses a flexible Markov random field approach to segmentation that allows for a wider range of spot shapes than existing methods, including relatively common ‘doughnut-shaped’ spots; (b) models the image directly as background plus hybridization intensity, and estimates the two quantities simultaneously, avoiding the common logical error that estimates of foreground may be less than those of the corresponding background if the two are estimated separately; and (c) uses a probabilistic modeling approach to simultaneously perform segmentation and intensity estimation, and to compute spot quality measures. We describe two approaches to parameter estimation: a fast algorithm, based on the expectation-maximization and the iterated conditional modes algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to offer some advantages over other methods. We use an HIV experiment to compare our approach to two commercial software products: Spot and Arrayvision.
Keywords: Bayesian estimation; cDNA microarrays; Expectation-maximization; Gene expression; Hierarchical- t; Image analysis; Iterated conditional modes; Markov chain Monte Carlo; Markov random fields; Quality measures; Segmentation; Spatial statistics
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