Biclustering of Expression Microarray Data Using Affinity Propagation

  • Alessandro Farinelli
  • Matteo Denitto
  • Manuele Bicego
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)


Biclustering, namely simultaneous clustering of genes and samples, represents a challenging and important research line in the expression microarray data analysis. In this paper, we investigate the use of Affinity Propagation, a popular clustering method, to perform biclustering. Specifically, we cast Affinity Propagation into the Couple Two Way Clustering scheme, which allows to use a clustering technique to perform biclustering. We extend the CTWC approach, adapting it to Affinity Propagation, by introducing a stability criterion and by devising an approach to automatically assemble couples of stable clusters into biclusters.

Empirical results, obtained in a synthetic benchmark for biclustering, show that our approach is extremely competitive with respect to the state of the art, achieving an accuracy of 91% in the worst case performance and 100% accuracy for all tested noise levels in the best case.


Stable Cluster Median Absolute Deviation Expression Microarray Data Synthetic Benchmark Column Cluster 
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.


  1. 1.
    Bay, A., Granitto, P.: Clustering gene expression data with a penalized graph-based metric. BMC Bioinformatics 12 (2011)Google Scholar
  2. 2.
    Bicego, M., Lovato, P., Ferrarini, A., Delledonne, M.: Biclustering of expression microarray data with topic models. In: Proceedings of the International Conference on Pattern Recognition, pp. 2728–2731 (2010)Google Scholar
  3. 3.
    Bicego, M., Lovato, P., Oliboni, B., Perina, A.: Expression microarray classification using topic models. In: ACM Symposium on Applied Computing (Bioinformatics and Computational Biology track) (2010)Google Scholar
  4. 4.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  5. 5.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  6. 6.
    Brändle, N., Bischof, H., Lapp, H.: Robust DNA microarray image analysis. Machine Vision and Applications 15, 11–28 (2003)CrossRefGoogle Scholar
  7. 7.
    Chiu, T.Y., Hsu, T.C., Wang, J.S.: Ap-based consensus clustering for gene expression time series. In: Proc. Int. Conf. on Pattern Recognition, pp. 2512–2515 (2010)Google Scholar
  8. 8.
    de Souto, M., Costa, I., de Araujo, D., Ludermir, T., Schliep, A.: Clustering cancer gene expression data: A comparative study. BMC Bioinformatics 9 (2008)Google Scholar
  9. 9.
    Felsenstein, J.: Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791 (1985)CrossRefGoogle Scholar
  10. 10.
    Frey, B., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Getz, G., Levine, E., Domany, E.: Coupled two-way clustering analysis of gene microarray data. Proc. Natl. Acad. Sci. USA 97(22), 12079–12084 (2000)CrossRefGoogle Scholar
  12. 12.
    Givoni, I., Frey, B.: A binary variable model for affinity propagation. Neural Computation 21(6), 1589–1600 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Hampel, F., Rousseeuw, P., Ronchetti, E., Stahel, W.: Robust Statistics: the Approach Based on Influence Functions. John Wiley & Sons (1986)Google Scholar
  14. 14.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1-2), 177–196 (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Jain, A., Dubes, R.: Algorithms for clustering data. Prentice-Hall (1988)Google Scholar
  16. 16.
    Kiddle, S., Windram, O., McHattie, S., Mead, A., Beynon, J., Buchanan-Wollaston, V., Denby, K., Mukherjee, S.: Temporal clustering by affinity propagation reveals transcriptional modules in arabidopsis thaliana. Bioinformatics 26(3), 355–362 (2010)CrossRefGoogle Scholar
  17. 17.
    Kschischang, F., Frey, B., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47(2), 498–519 (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Lee, J.W., Lee, J.B., Park, M., Song, S.: An extensive comparison of recent classification tools applied to microarray data. Computational Statistics & Data Analysis 48(4), 869–885 (2005)CrossRefzbMATHGoogle Scholar
  19. 19.
    Leone, M., Weigt, S., Weigt, M.: Clustering by soft-constraint affinity propagation: applications to gene-expression data. Bioinformatics 23(20), 2708–2715 (2007)CrossRefGoogle Scholar
  20. 20.
    Madeira, S., Oliveira, A.: Biclustering algorithms for biological data analysis: a survey. IEEE Trans. on Computational Biology and Bioinformatics 1, 24–44 (2004)CrossRefGoogle Scholar
  21. 21.
    Perina, A., Lovato, P., Murino, V., Bicego, M.: Biologically-aware Latent Dirichlet Allocation (BaLDA) for the Classification of Expression Microarray. In: Dijkstra, T.M.H., Tsivtsivadze, E., Marchiori, E., Heskes, T. (eds.) PRIB 2010. LNCS, vol. 6282, pp. 230–241. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)CrossRefGoogle Scholar
  23. 23.
    Rogers, S., Girolami, M., Campbell, C., Breitling, R.: The latent process decomposition of cdna microarray data sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2(2), 143–156 (2005)CrossRefGoogle Scholar
  24. 24.
    Statnikov, A., Aliferis, C., Tsamardinos, I., Hardin, D., Levy, S.: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5), 631–643 (2005)CrossRefGoogle Scholar
  25. 25.
    Valafar, F.: Pattern recognition techniques in microarray data analysis: A survey. Annals of the New York Academy of Sciences 980, 41–64 (2002)CrossRefGoogle Scholar
  26. 26.
    Zhang, X., Wu, F., Zhuang, Y.: Clustering by evidence accumulation on affinity propagation. In: Proc. Int. Conf. on Pattern Recognition, pp. 1–4 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alessandro Farinelli
    • 1
  • Matteo Denitto
    • 1
  • Manuele Bicego
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly

Personalised recommendations