Advertisement

Optimal Selection of Microarray Analysis Methods Using a Conceptual Clustering Algorithm

  • C. Rubio-Escudero
  • R. Romero-Záliz
  • O. Cordón
  • O. Harari
  • C. del Val
  • I. Zwir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

The rapid development of methods that select over/under expressed genes from microarray experiments have not yet matched the need for tools that identify informational profiles that differentiate between experimental conditions such as time, treatment and phenotype. Uncertainty arises when methods devoted to identify significantly expressed genes are evaluated: do all microarray analysis methods yield similar results from the same input dataset? do different microarray datasets require distinct analysis methods?. We performed a detailed evaluation of several microarray analysis methods, finding that none of these methods alone identifies all observable differential profiles, nor subsumes the results obtained by the other methods. Consequently, we propose a procedure that, given certain user-defined preferences, generates an optimal suite of statistical methods. These solutions are optimal in the sense that they constitute partial ordered subsets of all possible method-associations bounded by both, the most specific and the most sensitive available solution.

Keywords

Association Rule Microarray Experiment Multiobjective Optimization Microarray Dataset Pareto Optimal Front 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)MATHCrossRefGoogle Scholar
  2. 2.
    Brown, P., Botstein, D.: Exploring the new world of the genome with DNA microarrays. Nature Genet. 21(suppl.), 33–37 (1999)CrossRefGoogle Scholar
  3. 3.
    Pan, W., Lin, J., Le, C.: A mixture model approach to detecting differentially expressed genes with microarray data. Funct. Integr. Genomics 3(3), 117–124 (2001)Google Scholar
  4. 4.
    Li, C., Wong, W.H.: DNA-Chip Analyzer (dChip). In: Parmigiani, G., Garrett, E.S., Irizarry, R., Zeger, S.L. (eds.) The analysis of gene expression data: methods and software. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA. 98, 5116–5121 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Park, T., Yi, S.G., Lee, S., Lee, S.Y., Yoo, D.H., Ahn, J.I., Lee, Y.S.: Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 19(6), 694–703 (2003)CrossRefGoogle Scholar
  7. 7.
    Der, G., Everitt, B.S.: Handbook of Statistical Analyses using SAS. Chapman and Hall/CRC (2001)Google Scholar
  8. 8.
    Cheeseman, P., Oldford, R.W.: Selecting models from data: artificial intelligence and statistics IV. Springer, Heidelberg (1994)MATHGoogle Scholar
  9. 9.
    Zwir, I., Shin, D., Kato, A., Nishino, K., Latifi, K., Solomon, F., Hare, J.M., Huang, H., Groisman, E.A.: Dissecting the PhoP regulatory network of Escherichia coli and Salmonella enterica. Proc. Natl. Acad. Sci. 102, 2862–2867 (2005a)CrossRefGoogle Scholar
  10. 10.
    Zwir, I., Huang, H., Groisman, E.A.: Analysis of Differentially-Regulated Genes within a Regulatory Network by GPS Genome Navigation, Bioinformatics (2005b) (in press)Google Scholar
  11. 11.
    Chankong, V., Haimes, Y.Y.: Multiobjective decision making theory and methodology. North-Holland, Amsterdam (1983)MATHGoogle Scholar
  12. 12.
    Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, Chichester, New York (2001)MATHGoogle Scholar
  13. 13.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings of the ACM SIGMOD. International Conference on Management of Data, Washington, D.C, pp. 207–216 (1993)Google Scholar
  14. 14.
    Kooperberg, C., Sipione, S., LeBlanc, M., Strand, A.D., Cattaneo, E., Olson, J.M.: Evaluating test statistics to select interesting genes in microarray experiments. Hum. Mol. Genet. 11(19), 2223–2232 (2002)CrossRefGoogle Scholar
  15. 15.
    Mitchell, T.: Machine Learning. McGraw-Hill, New York (1997)MATHGoogle Scholar
  16. 16.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons, Chichester (1973)MATHGoogle Scholar
  17. 17.
    Cordón, O., del Jesus, M.J., Herrera, F.: A Proposal on Reasoning Methods in Fuzzy Rule-Based Classification Systems. International Journal of Approximate Reasoning 20, 21–45 (1999)Google Scholar
  18. 18.
    Calvano, S.E., Xiao, W., Richards, D.R., Feliciano, R.M., Baker, H.V., Cho, R.J., Chen, R.O., Brownstein, B.H., Cobb, J.P., Tschoeke, S.K., Miller-Graziano, C., Moldawer, L.L., Mindrinos, M.N., Davis, R.W., Tompkins, R.G., Lowry, S.F.: The Inflammation and Host Response to Injury Large Scale Collaborative Research Program. In: A Network- Based Analysis of Systemic Inflammation in Humans. Nature (2005) (in press)Google Scholar
  19. 19.
    Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture, Nat. Genet. 22, 281–285 (1999)Google Scholar
  20. 20.
    Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubin, G.M., Sherlock, G.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat. Genet. 25, 25–29 (2000)Google Scholar
  21. 21.
    Benitez-Bellon, E., Moreno-Hagelsieb, G., Collado-Vides, J.: Evaluation of thresholds for the detection of binding sites for regulatory proteins in Escherichia coli K12 DNA. Genome Biol. 3(3) RESEARCH0013 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • C. Rubio-Escudero
    • 1
  • R. Romero-Záliz
    • 1
  • O. Cordón
    • 1
  • O. Harari
    • 1
  • C. del Val
    • 1
  • I. Zwir
    • 1
    • 2
  1. 1.Department of Computer Science and Artificial IntelligenceGranadaSpain
  2. 2.Howard Hughes Medical InstituteWashington University School of MedicineSt. Louis

Personalised recommendations