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Applied Bioinformatics

, Volume 4, Issue 1, pp 1–11 | Cite as

Spot Detection and Image Segmentation in DNA Microarray Data

  • Li Qin
  • Luis RuedaEmail author
  • Adnan Ali
  • Alioune Ngom
Methodology

Abstract

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.

Keywords

Image Segmentation Background Correction Foreground Pixel Microarray Image Noisy Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

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..

References

  1. 1.
    Brown P, Botstein D. Exploring the new world of the genome with DNA microarrays. Nat Genet 1999 Jan; 21 (1 Suppl.): 33–7PubMedCrossRefGoogle Scholar
  2. 2.
    Bittner M, Meltzer XP, Chen XY, et al. Gene expression data analysis. FEBS Lett 2000; 480: 17–24CrossRefGoogle Scholar
  3. 3.
    Schena M. Microarray analysis. Hoboken (NJ): Wiley-Liss, 2003Google Scholar
  4. 4.
    Eisen M. ScanAlyze user manual. Stanford (CA): Stanford University, 1999Google Scholar
  5. 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. 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]
  7. 7.
    Buckly M. The Spot user’s guide [online]. CSIRO Mathematical and Information Sciences, 2000. Available from URL: http://www.cmis.csiro.au/IAP/Spot/spotmanual.htm [Accessed 2005 May 24]Google Scholar
  8. 8.
    Callow MJ, Dudoit S, Gong EL, et al. Microarray expression profiling identifies genes with altered expression in HDL deficient mice. Genome Res 2000; 10(12): 2022–9PubMedCrossRefGoogle Scholar
  9. 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
  10. 10.
    Jain A, Tokuyasu T, Snijders A, et al. Fully automatic quantification of microarray image data. Genome Res 2002; 12(2): 325–32PubMedCrossRefGoogle Scholar
  11. 11.
    Steinfath M, Wruck W, Scidel H. Automated image analysis for array hybridization experiments. Bioinformatics 2001; 17(7): 634–41PubMedCrossRefGoogle Scholar
  12. 12.
    Soille P. Morphological image analysis: principles and applications. 2nd ed. New York: Springer-Verlag, 2003Google Scholar
  13. 13.
    Chen Y, Dougherty E, Bittner M. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 1997; 2: 364–74PubMedCrossRefGoogle Scholar
  14. 14.
    Kooperberg C, Fazzio T, Tsukiyama T. Improved background correction for spotted DNA microarrays. J Comput Biol 2002; 9(1): 55–66PubMedCrossRefGoogle Scholar
  15. 15.
    Goryachev A, Macgregor P, Edwards A. Unfolding of microarray data. J Comput Biol 2001; 8(4): 443–61PubMedCrossRefGoogle Scholar
  16. 16.
    Yang M, Ruan Q, Yang J, et al. A statistical procedure for flagging weak spots greatly improves normalization and ratio estimates in microarray experiments. Physiol Genomics 2001; 7(1): 45–53PubMedGoogle Scholar
  17. 17.
    Schuchhardt J, Beule D, Malik A, et al. Normalization strategies for cDNA microarrays. Nucleic Acids Res 2000; 28: 47CrossRefGoogle Scholar
  18. 18.
    Duda R, Hart P, Stork D. Pattern classification. 2nd ed. Canada: Wiley-Interscience, 2000Google Scholar
  19. 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. 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
  21. 21.
    Spellman P, Sherlock G, Zhang M, et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998; 9: 3273–97PubMedGoogle Scholar
  22. 22.
    Zien A, Ratsch G, Mika S, et al. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 2000; 16(9): 799–807PubMedCrossRefGoogle Scholar
  23. 23.
    Cai Y, Liu X, Xu X, et al. Support vector machines for predicting protein structural class. BMC Bioinformatics 2001; 2(1): 1–5CrossRefGoogle Scholar
  24. 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
  25. 25.
    Ding C, Dubchak I. Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 2001; 17: 349–58PubMedCrossRefGoogle Scholar
  26. 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
  27. 27.
    Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machines. Mach Learn 2002; 46(1/3): 389–422CrossRefGoogle Scholar
  28. 28.
    Rueda L, Oommen BJ. On optimal pairwise linear classifiers for normal distributions: the two-dimensional case. IEEE Trans Pattern Anal Mach Intell 2002; 24(2): 274–80CrossRefGoogle Scholar
  29. 29.
    Rueda L. An efficient approach to compute the threshold in multi-dimensional linear classifiers. Pattern Recognit 2004; 37(4): 811–26CrossRefGoogle Scholar
  30. 30.
    Wen X, Fuhrman S, Michaels G, et al. Large-scale temporal gene expression mapping of central nervous system development. Proc Natl Acad Sci U S A 1998; 95: 334–9PubMedCrossRefGoogle Scholar
  31. 31.
    Alon U, Barkai N, Notterman D, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon cancer tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A 1999 Jun 8; 96(12): 6745–50PubMedCrossRefGoogle Scholar
  32. 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
  33. 33.
    Perou C, Jeffrey S, van de Rijn M, et al. Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci U S A 1999; 96: 9212–7PubMedCrossRefGoogle Scholar
  34. 34.
    Furey T, Cristianini N, Duffy N, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000; 16(10): 906–14PubMedCrossRefGoogle Scholar
  35. 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
  36. 36.
    Tamayo P, Slonim D, Mesirov J, et al. Interpreting patterns of gene expression with selforganizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A 1999; 96(6): 2907–12PubMedCrossRefGoogle Scholar
  37. 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. 38.
    Puzicha J, Buhmann J, Hofmann T. Histogram clustering for unsupervised image segmentation. Comput Vis Pattern Recognit 1999; 2: 2602–8Google Scholar
  39. 39.
    Draghici S. Data analysis for DNA microarrays. Boca Raton (FL): CRC Press, 2003CrossRefGoogle Scholar
  40. 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
  41. 41.
    Heyer L, Moskowitz D, Abele J, et al. MAGIC tool: integrated microarray data analysis. Bioinformatics 2005; 21(9): 2114–5PubMedCrossRefGoogle Scholar
  42. 42.
    Adams R, Bishop L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell 1994; 16(6): 641–7CrossRefGoogle Scholar
  43. 43.
    Yang Y, Buckley M, Dudoit S, et al. Comparison of methods for image analysis on cDNA microarray data. J Comput Graph Stat 2002; 11: 108–36CrossRefGoogle Scholar
  44. 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. 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

Copyright information

© Adis Data Information BV 2005

Authors and Affiliations

  1. 1.IBM Canada LtdMarkhamCanada
  2. 2.School of Computer ScienceUniversity of WindsorWindsorCanada
  3. 3.Department of Biological SciencesUniversity of WindsorWindsorCanada

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