Microarray subgrid detection: a novel algorithm

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.

[1]  Horst Bischof,et al.  Automatic Grid Fitting for Genetic Spot Array Images Containing Guide Spots , 1999, CAIP.

[2]  Jörg Rahnenführer,et al.  Hybrid clustering for microarray image analysis combining intensity and shape features , 2004, BMC Bioinformatics.

[3]  Junior Barrera,et al.  Microarray gridding by mathematical morphology , 2001, Proceedings XIV Brazilian Symposium on Computer Graphics and Image Processing.

[4]  Yuan-Kai Wang,et al.  DNA microarray image analysis using active contour model , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

[5]  Peter Bajcsy.  Gridline: automatic grid alignment DNA microarray scans , 2004, IEEE Transactions on Image Processing.

[6]  Nikolas P. Galatsanos,et al.  An unsupervised artifact correction approach for the analysis of DNA microarray images , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[7]  Daniel Morris,et al.  Blind Microarray Gridding: A New Framework , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  K Fraser,et al.  Copasetic analysis: a framework for the blind analysis of microarray imagery. , 2004, Systems biology.

[9]  Giuliano Antoniol,et al.  Microarray image addressing based on the Radon transform , 2005, IEEE International Conference on Image Processing 2005.

[10]  Adrian E. Raftery,et al.  Donuts, scratches and blanks: robust model-based segmentation of microarray images , 2005, Bioinform..

[11]  D. Bozinov.  Autonomous system for web-based microarray image analysis , 2003, IEEE Transactions on NanoBioscience.

[12]  Yu Luo,et al.  Gridding and compression of microarray images , 2004, Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004..

[13]  Hans Lehrach,et al.  Automated image analysis for array hybridization experiments , 2001, Bioinform..

[14]  R. Fabbri,et al.  Towards non-parametric gridding of microarray images , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[15]  Peter Bajcsy,et al.  An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches , 2006, EURASIP J. Adv. Signal Process..