Spot Detection and Image Segmentation in DNA Microarray Data
- 29 Downloads
Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance.
KeywordsImage Segmentation Background Correction Foreground Pixel Microarray Image Noisy Pixel
The authors would like to thank the referees who devoted their efforts to substantially improving the quality of the paper. This research work has been partially supported by NSERC (Natural Sciences and Engineering Council of Canada), CFI (Canadian Foundation for Innovation) and OIT (Ontario Innovation Trust).
The authors have provided no information on conflicts of interest directly relevant to the content of this article..
- 3.Schena M. Microarray analysis. Hoboken (NJ): Wiley-Liss, 2003Google Scholar
- 4.Eisen M. ScanAlyze user manual. Stanford (CA): Stanford University, 1999Google Scholar
- 5.Axon Instruments, Inc. GenePix Professional 4200A: microarray scanner user’s guide [online]. Available from URL: http://www.files.axon.com/downloads/manuals/GenePix_4200A_User_Guide_Rev_B.pdf [Accessed 2005 May 24]
- 6.Packard BioScience. QuantArray microarray analysis software manual [online]. Available from URL: http://www.las.perkinelmer.com/content/Manuals/quantarraymanual.pdf [Accessed 2005 May 24]
- 9.Katzer M, Kummert F, Sagerer G. Robust automatic microarray image analysis. International Conference on Bioinformatics: North-South Networking; 2002 Feb 6–8; BangkokGoogle Scholar
- 12.Soille P. Morphological image analysis: principles and applications. 2nd ed. New York: Springer-Verlag, 2003Google Scholar
- 18.Duda R, Hart P, Stork D. Pattern classification. 2nd ed. Canada: Wiley-Interscience, 2000Google Scholar
- 19.Jaakkola T, Diekhans M, Haussler D. Using the Fisher kernel method to detect remote protein homologies. Proc Int Conf Intell Syst Mol Biol 1999, 149–58Google Scholar
- 20.Mukherjee S, Tamayo P, Slonim D, et al. Support vector machine classification of microarray data. Artificial Intelligence (AI) Memo 1677. Cambridge (MA): Massachusetts Institute of Technology, 1999Google Scholar
- 24.Schölkopf B, Guyon IM, Weston J. Statistical learning and kernel methods in bioinformatics. In: Frasconi P, Shamir R, editors. Artificial intelligence and heuristic methods in bioinformatics. Amsterdam: IOS Press, 2003: 1–21Google Scholar
- 26.Campanini R, Dongiovanni D, Lanconelli N, et al. A support vector machines classifier based on recursive feature elimination for microarray data in breast cancer characterization. First National Workshop on Bioinformatics, VIII National Congress of the Italian Association for Artificial Intelligence; 2002 Sep 10; Siena, ItalyGoogle Scholar
- 32.Tibshirani R, Hastie T, Eisen M, et al. Clustering methods for the analysis of DNA microarray data [technical report]. Stanford (CA): Department of Statistics, Stanford University, 1999Google Scholar
- 35.Bicciato S, Pandin M, Didone G, et al. Analysis of an associative memory neural network for pattern identification in gene expression data. Workshop on Data Mining and Bioinformatics (BIOKDD’01); 2001 Aug 26; San FranciscoGoogle Scholar
- 37.Asano T, Chen D, Katoh N, et al. Polynomial-time solutions to image segmentation. Proceedings of the Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia: Society of Applied and Industrial Mathematics, 1996Google Scholar
- 38.Puzicha J, Buhmann J, Hofmann T. Histogram clustering for unsupervised image segmentation. Comput Vis Pattern Recognit 1999; 2: 2602–8Google Scholar
- 40.Buhler J, Ideker T, Haynor D. Dapple: improved techniques for finding spots on DNA microarrays [technical report UWTR 2000-08-05.]. Seattle: University of Washington, 2000Google Scholar
- 44.Wu H, Yan H. Microarray image processing based on clustering and morphological analysis. Proceedings of the First Asia-Pacific Conference on Bioinformatics. Darlinghurst, Australia: Australian Computer Society, Inc., 2003: 111–8Google Scholar
- 45.Rueda L, Qin L. An unsupervised learning scheme for DNA microarray image spot detection. First International Conference on Complex Medical Engineering; 2005 May 15–18; Takamatsu, JapanGoogle Scholar