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Spot Detection and Image Segmentation in DNA Microarray Data

  • Methodology
  • Published:
Applied Bioinformatics

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.

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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..

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Authors and Affiliations

  1. IBM Canada Ltd, Markham, Ontario, Canada

    Li Qin

  2. School of Computer Science, University of Windsor, 401 Sunset Ave, Windsor, Ontario, N9B 3P4, Canada

    Luis Rueda & Alioune Ngom

  3. Department of Biological Sciences, University of Windsor, Windsor, Ontario, Canada

    Adnan Ali

Authors
  1. Li Qin
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  2. Luis Rueda
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  3. Adnan Ali
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  4. Alioune Ngom
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Corresponding author

Correspondence to Luis Rueda.

Appendix 1

Appendix 1

Refer to figure A1.

Theorem 1:Let D = {x 1,…,x n}, which has to be clustered into two classes. If n→∞, KSCMIS produces the same results as the k-means algorithm, where the Euclidean distance is used.

Proof: Let n = N 1 + N 2. Since empty clusters are not allowed, then it is true that as n→∞, it implies that N 1, N 2 →∞. We can then write the asymptotic behaviour of equation 4 as follows (equation 6):

Additionally, it is straightforward that, in the 1-D Euclidean space, equation 6 is equivalent to (x i —μ(1)2 > (x i — μ1)2. Clearly, equation 6 is the criterion used by the standard k-means algorithm, and thus the result follows.

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Qin, L., Rueda, L., Ali, A. et al. Spot Detection and Image Segmentation in DNA Microarray Data. Appl-Bioinformatics 4, 1–11 (2005). https://doi.org/10.2165/00822942-200504010-00001

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