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Quantitative Improvements in cDNA Microarray Spot Segmentation

  • Mónica G. Larese
  • Juan Carlos Gómez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5676)

Abstract

When developing a cDNA microarray experiment, the segmentation of individual spots is a crucial stage. Spot intensity measurements and gene expression ratios directly depend on the effectiveness and accuracy of the segmentation results. However, since the ground truth is unknown in microarray experiments, quantification of the accuracy of the segmentation process is a very difficult task. In this paper an improved unsupervised technique based on the combination of clustering algorithms and Markov Random Fields (MRF) is proposed to separate the foreground and background intensity signals used in the spot ratio computation. The segmentation algorithm uses one of two alternative methods to provide for initialization, namely K-means and Gaussian Mixture Models (GMM) clustering. This initial segmentation is then processed via MRF. Accuracy is measured by means of a set of microarray images containing spike spots where the target ratios are known a priori, thus making it possible to quantify the expression ratio errors. Results show improvements over state-of-the-art procedures.

Keywords

Gene expression cDNA microarray segmentation Gaussian Mixture Models K-means clustering Markov Random Field segmentation quantitative segmentation errors 

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References

  1. 1.
    Wang, Y.P., Gunampally, M.R., Cai, W.W.: Automated segmentation of microarray spots using fuzzy clustering approaches. In: IEEE Workshop on Machine Learning for Signal Processing, pp. 387–391 (2005)Google Scholar
  2. 2.
    Gottardo, R., Besag, J., Stephens, M., Murua, A.: Probabilistic segmentation and intensity estimation for microarray images. Biostatistics 7(1), 85–99 (2006)CrossRefPubMedGoogle Scholar
  3. 3.
    Demirkaya, O., et al.: Segmentation of cDNA microarray spots using Markov Random Field modeling. Bioinformatics 21(13), 2994–3000 (2005)CrossRefPubMedGoogle Scholar
  4. 4.
    Lehmussola, A., Ruusuvuori, P., Yli Harja, O.: Evaluating the performance of microarray segmentation algorithms. Bioinformatics 22(23), 2910–2917 (2006)CrossRefPubMedGoogle Scholar
  5. 5.
    Nykter, M., Aho, T., et al.: Simulation of microarray data with realistic characteristics. BMC Bioinformatics 7(349), 1–17 (2006)Google Scholar
  6. 6.
    Chen, T.B., Lu, H.H.S., et al.: Segmentation of cDNA microarray images by kernel density estimation. J. of Biomedical Informatics 41, 1021–1027 (2008)CrossRefPubMedGoogle Scholar
  7. 7.
    Wang, T., Lee, Y., et al.: Establishment of cDNA microarray analysis at the Genomic Medicine Research Core Laboratory (GMRCL) of Chang Gung Memorial Hospital. Chang Gung Med. Journal 27(4), 243–260 (2004)Google Scholar
  8. 8.
    Chao, A., Wang, T.H., et al.: Molecular characterization of adenocarcinoma and squamous carcinoma of the uterine cervix using microarray analysis of gene expression. Int. J. Cancer 119(1), 91–98 (2006)CrossRefPubMedGoogle Scholar
  9. 9.
    Bozinov, D., Rahnenfürher, J.: Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering. Bioinformatics 18(5), 747–756 (2002)CrossRefPubMedGoogle Scholar
  10. 10.
    Blekas, K., Galatsanos, N.P., et al.: Mixture model analysis of DNA microarray images. IEEE Transactions on Medical Imaging 24(7), 901–909 (2005)CrossRefPubMedGoogle Scholar
  11. 11.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)Google Scholar
  12. 12.
    Blekas, K., Galatsanos, N.P., et al.: An unsupervised artifact correction approach for the analysis of DNA microarray images. In: IEEE ICIP, Barcelona, pp. 165–168 (2003)Google Scholar
  13. 13.
    Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, Heidelberg (2001)Google Scholar
  14. 14.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. on P.A.M.I. 6, 721–741 (1984)CrossRefGoogle Scholar
  15. 15.
    Besag, J.: On the statistical analysis of dirty images. J. of the Royal Statistical Soc. Series B 48(3), 259–302 (1986)Google Scholar
  16. 16.
    Sonka, M., Hlavac, V., Boyle, R.: Image processing analysis and machine vision. Thomson (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mónica G. Larese
    • 1
    • 2
  • Juan Carlos Gómez
    • 1
    • 2
  1. 1.Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas CIFASIS-CONICETRosarioArgentina
  2. 2.Laboratory for System Dynamics and Signal ProcessingFCEIA, UNRRosarioArgentina

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