Microarray subgrid detection: a novel algorithm

Authors:
Daniel Morris;Zidong Wang;Xiaohui Liu
Affiliations:
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UK;Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UK;Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex, UK
Venue:
International Journal of Computer Mathematics - Bioinformatics
Year:
2007

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Abstract

A novel algorithm for detecting microarray subgrids is proposed. The only input to the algorithm is the raw microarray image, which can be of any resolution, and the subgrid detection is performed with no prior assumptions. The algorithm consists of a series of methods of spot shape detection, spot filtering, spot spacing estimation, and subgrid shape detection. It is shown to be able to divide images of varying quality into subgrid regions with no manual interaction. The algorithm is robust against high levels of noise and high percentages of poorly expressed or missing spots. In addition, it is proved to be effective in locating regular groupings of primitives in a set of non-microarray images, suggesting potential application in the general area of image processing.