A Markov Random Field Model of Microarray Gridding

DNA microarray hybridisation is a popular high throughput 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 dat...

• Type: Conference
• Year: 2003
• Where: SAC
• Authors: Mathias Katzer, Franz Kummert, Gerhard Sagerer
• Keywords: Applied Computing, DNA Microarray Hybridisation, Microarray Gridding, Microarray Images, SAC 2003