A Markov Random Field model of microarray gridding

ABSTRACT

DNA microarray hybridisation is a popular high through-put technique in academic as well as industrial functional genomics research. In this paper we present a new approach to automatic grid segmentation of the raw fluorescence microarray images by Markov Random Field (MRF) techniques. The main objectives are applicability to various types of array designs and robustness to the typical problems encountered in microarray images, which are contaminations and weak signal.We briefly introduce microarray technology and give some background on MRFs. Our MRF model of microarray gridding is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm. We show how to compute the model components efficiently and state our deterministic MRF energy minimization algorithm that was derived from the 'Highest Confidence First' algorithm by Chou et al. Since MRF segmentation may fail due to the properties of the data and the minimization algorithm, we use supplied or estimated print layouts to validate results.Finally we present results of tests on several series of microarray images from different sources, some of them test sets published with other microarray gridding software. Our MRF grid segmentation requires weaker assumptions about the array printing process than previously published methods and produces excellent results on many real datasets.An implementation of the described methods is available upon request from the authors.

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