Advertisement

Merge Method for Shape-Based Clustering in Time Series Microarray Analysis

  • Irene Barbero
  • Camelia Chira
  • Javier Sedano
  • Carlos Prieto
  • José R. Villar
  • Emilio Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7435)

Abstract

A challenging task in time-course microarray data analysis is to combine the information provided by multiple time series in order to cluster genes meaningfully. This paper proposes a novel merge method to accomplish this goal obtaining clusters with highly correlated genes. The main idea of the proposed method is to generate a clustering, starting from clusterings created from different time series individually, that takes into account the number of times each clustering assemble two genes into the same group. Computational experiments are performed for real-world time series microarray with the purpose of finding co-expressed genes related to the production and growth of a certain bacteria. The results obtained by the introduced merge method are compared with clusterings generated by time series individually and averaged as well as interpreted biologically.

Keywords

microarray analysis time series clustering merge methods 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lee, C.-P., Lin, W.-S., Chen, Y.-M., Kuo, B.-J.: Gene selection and sample classification on microarray data based on adaptive genetic algorithm/k-nearest neighbor method. Expert Systems with Applications 38(5), 4661–4667 (2011)CrossRefGoogle Scholar
  2. 2.
    Liu, H., Liu, L., Zhang, H.: Ensemble gene selection by grouping for microarray data classification. Journal of Biomedical Informatics 43(1), 81–87 (2010); PMID: 19699316CrossRefGoogle Scholar
  3. 3.
    Wang, Y., Tetko, I.V., Hall, M.A., Frank, E., Facius, A., Mayer, K.F.X., Mewes, H.W.: Gene selection from microarray data for cancer classification–a machine learning approach. Computational Biology and Chemistry 29(1), 37–46 (2005)zbMATHCrossRefGoogle Scholar
  4. 4.
    Wei, J.S., Greer, B.T., Westermann, F., Steinberg, S.M., Son, C.-G., Chen, Q.-R., Whiteford, C.C., Bilke, S., Krasnoselsky, A.L., Cenacchi, N., Catchpoole, D., Berthold, F., Schwab, M., Khan, J.: Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma. Cancer Research 64(19), 6883–6891 (2004)CrossRefGoogle Scholar
  5. 5.
    Coffey, N., Hinde, J.: Analyzing time-course microarray data using functional data analysis - a review. Statistical Applications in Genetics and Molecular Biology 10 (2011); peer-reviewedGoogle Scholar
  6. 6.
    Krishna, R., Li, C.-T., Buchanan-Wollaston, V.: A temporal precedence based clustering method for gene expression microarray data. BMC Bioinformatics 11(1), 68 (2010)CrossRefGoogle Scholar
  7. 7.
    Yi, S.-G., Joo, Y.-J., Park, T.: Rank-based clustering analysis for the time-course microarray data. Journal of Bioinformatics and Computational Biology 7(1), 75–91 (2009); PMID: 19226661CrossRefGoogle Scholar
  8. 8.
    Storey, J., Xiao, W., Leek, J., Tompkins, R., Davis, R.: Significance analysis of time course microarray experiments. UW Biostatistics Working Paper Series (August 2004)Google Scholar
  9. 9.
    Wolfe, C.J., Kohane, I.S., Butte, A.J.: Systematic survey reveals general applicability of ”guilt-by-association” within gene coexpression networks. BMC Bioinformatics 6, 227 (2005); PMID: 16162296CrossRefGoogle Scholar
  10. 10.
    Phan, S., Famili, F., Tang, Z., Pan, Y., Liu, Z., Ouyang, J., Lenferink, A., Mc-Court O’connor, M.: A novel pattern based clustering methodology for time-series microarray data. International Journal of Computer Mathematics 84(5), 585–597 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Smyth, G.K., Speed, T.: Normalization of cdna microarray data. Methods 31(4), 265–273 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Irene Barbero
    • 1
  • Camelia Chira
    • 1
    • 2
  • Javier Sedano
    • 1
    • 3
  • Carlos Prieto
    • 4
  • José R. Villar
    • 5
  • Emilio Corchado
    • 6
  1. 1.Instituto Tecnológico de Castilla y LeónBurgosSpain
  2. 2.Department of Computer ScienceBabes-Bolyai UniversityCluj-NapocaRomania
  3. 3.Department of Electromechanical EngineeringUniversity of BurgosSpain
  4. 4.Instituto de Biotecnología de LeónSpain
  5. 5.University of OviedoGijónSpain
  6. 6.University of SalamancaSpain

Personalised recommendations