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Biostatistics Advance Access published online on July 27, 2005
Biostatistics, doi:10.1093/biostatistics/kxi042
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.
Received November 12, 2004
Revised July 6, 2005
Accepted July 11, 2005

Article

Probabilistic Segmentation and Intensity Estimation for Microarray Images

Raphael Gottardo 1*, Julian Besag 1, Matthew Stephens 1, and Alejandro Murua 1

1 Department of Statistics, University of Washington, Box 354322 Seattle, WA 98195-4322

* To whom correspondence should be addressed.
Raphael Gottardo, E-mail: raph{at}stat.washington.edu


  Abstract

We describe a probabilistic approach to simultaneous image segmentation and intensity estimation for cDNA microarray experiments. The approach overcomes several limitations of existing methods. In particular it a) uses a exible 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 corresponding background if the two are estimated separately; c) uses a probabilistic modelling 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-Maximisation (EM) and the Iterated Conditional Modes (ICM) algorithms, and a fully Bayesian framework. These approaches produce comparable results, and both appear to other 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-Maximisation; 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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