δ-Clustering of Monotone Profiles

  • Adetayo Kasim
  • Suzy Van Sanden
  • Martin Otava
  • Sepp Hochreiter
  • Djork-Arné Clevert
  • Willem Talloen
  • Dan Lin
Chapter
Part of the Use R! book series (USE R)

Abstract

In Chaps. and 8, we discussed several testing procedures to detect differentially expressed genes with monotone relationship with respect to dose. The second question of primary interest in dose-response studies is the nature (or the shape of curve) of the dose-response relationship. In the context of dose-response microarray experiments, we wish to group (or classify) genes with similar dose-response relationship. Similar to the previous chapters, the subset of genes with monotone relationship is of interest.

References

  1. Amaratunga, D., Cabrera, J., & Kovtun, V. (2008). Microarray learning with ABC. Biostatistics, 9, 128–136.Google Scholar
  2. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of Royal Statistical Soceity B, 57, 289–300.Google Scholar
  3. Breiman, L. (1996) Random forests. Machine Learning, 24, 123–140.Google Scholar
  4. Calinski, R. B., & Harabasz, J. A. (1974). Dendrite method for cluster analysis. Communications in Statistics, 3, 1–27.Google Scholar
  5. Cheng, Y., & Church, G. M. (2000). Biclustering of expression data. Proceedings of the Conference on Intelligent Systems for Molecular Biology, 55, 93–104.Google Scholar
  6. Ge, Y., Dudoit, S., & Speed, P. T. (2003). Resampling based multiple testing for microarray data analysis (Technical report, 633). Berkeley: University of Berkeley.Google Scholar
  7. Hartigan, J. A., & Wong, M. A, (1979). Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics), 28(1), 100–108.Google Scholar
  8. Johnson, R. A., and Wichern, D. W.(2008). Applied Multivariate statistical analysis. Pearson.Google Scholar
  9. Kohonen, T. (2001), Self-Organizing Maps. 3rd edition, Springer-verlag, Berlin.Google Scholar
  10. Lin, D., Shkedy, Z., Yekutieli, D., Burzykowki, T., Göhlmann, H. W. H., De Bondt, A., et al. (2007). Testing for trend in dose-response microarray experiments: Comparison of several testing procedures, multiplicity, and resampling-based inference. Statistical Application in Genetics and Molecular Biology, 6(1). Article 26.Google Scholar
  11. Madeira, S. C., & Oliviera, A. L. (2004). Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 1(1), 24–45.Google Scholar
  12. Prelic, A., Bleuler, S., Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., et al. (2006). Systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics, 22(9), 1122–1129.Google Scholar
  13. Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society B, 63, 411–423.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Adetayo Kasim
    • 1
  • Suzy Van Sanden
    • 2
  • Martin Otava
    • 3
  • Sepp Hochreiter
    • 5
  • Djork-Arné Clevert
    • 5
  • Willem Talloen
    • 2
  • Dan Lin
    • 4
  1. 1.Wolfson Research InstituteDurham UniversityDurhamUK
  2. 2.Janssen Pharmaceutical Companies of Johnson & JohnsonBeerseBelgium
  3. 3.Interuniversity Institute for Biostatistics and Statistical Bioinformatics (I-BioStat), Center for Statistics (CenStat)Hasselt UniversityDiepenbeekBelgium
  4. 4.Veterinary Medicine Research and DevelopmentPfizer Animal HealthZaventemBelgium
  5. 5.Institute of BioinformaticsJohannes Kepler UniversityLinzAustria

Personalised recommendations