Advertisement

Bayesian Models for the Multi-sample Time-Course Microarray Experiments

  • Claudia Angelini
  • Daniela De Canditiis
  • Marianna Pensky
  • Naomi Brownstein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7548)

Abstract

In this paper we present a functional Bayesian method for detecting genes which are temporally differentially expressed between several conditions. We identify the nature of differential expression (e.g., gene is differentially expressed between the first and the second sample but is not differentially expressed between the second and the third) and subsequently we estimate gene expression temporal profiles. The proposed procedure deals successfully with various technical difficulties which arise in microarray time-course experiments such as a small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements. The procedure allows to account for various types of errors, thus, offering a good compromise between nonparametric and normality assumption based techniques. In addition, all evaluations are carried out using analytic expressions, hence, the entire procedure requires very small computational effort. The performance of the procedure is studied using simulated data.

Keywords

Bayesian analysis Classification Hypothesis testing Multi-sample problems Time-course microarray 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abramovich, F., Angelini, C.: Bayesian maximum a posteriori multiple testing procedure. Sankhya 68, 436–460 (2006)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Angelini, C., De Canditiis, D., Mutarelli, M., Pensky, M.: A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments. Stat. Appl. Gen. Mol. Bio. 6, Art. 24 (2007)Google Scholar
  3. 3.
    Angelini, C., De Canditiis, D., Pensky, M.: Bayesian Models for the Two-Sample Time-course Microarray Experiments. CSDA 53, 1547–1565 (2009)zbMATHGoogle Scholar
  4. 4.
    Bar–Joseph, Z.: Analyzing time series gene expression data. Bioinformatics 20, 2493–2503 (2004)CrossRefGoogle Scholar
  5. 5.
    Berger, O.J.: Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics (1985)Google Scholar
  6. 6.
    Conesa, A., Nueda, M.J., Ferrer, A., Talon, M.: MaSigPro: a method to identify significantly differential expression profiles in time-course microarray-experiments. Bioinformatics 22, 1096–1102 (2006)CrossRefGoogle Scholar
  7. 7.
    Cui, X., Kerr, M.K., Churchill, G.A.: Transformation for cDNA Microarray Data. Stat. Appl. Gen. Mol. Bio. 2 (2002)Google Scholar
  8. 8.
    Gradshteyn, I.S., Ryzhik, I.M.: Tables of Integrals, Series, and Products. Academic Press, New York (1980)Google Scholar
  9. 9.
    Heard, N.A., Holmes, C.C., Stephens, D.A.: A quantitative study of gene regulation involved in the Immune response of Anopheline Mosquitoes: An application of Bayesian hierarchical clustering of curves. JASA 101, 18–29 (2006)MathSciNetzbMATHGoogle Scholar
  10. 10.
    McLachlan, G., Do, K.A., Ambroise, C.: Analyzing microarray gene expression data. Wiley Series in Probability and Statistics (2004)Google Scholar
  11. 11.
    Müller, U., Schick, A., Wefelmeyer, W.: Estimating the error variance in nonparametric regression by a covariate-matched U-statistic. Statistics 37, 179–188 (2003)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Storey, J.D., Xiao, W., Leek, J.T., Tompkins, R.G., Davis, R.W.: Significance analysis of time course microarray experiments. PNAS 12, 12837–12842 (2005)CrossRefGoogle Scholar
  13. 13.
    Tai, Y.C., Speed, T.P.: On gene ranking using replicated microarray time course data. Biometrics 65, 40–51 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Vinciotti, V., Yu, K.: M-quantile regression analysis of temporal gene expression data. Stat. Stat. Appl. Gen. Mol. Bio. 8, Art. 41 (2009)Google Scholar
  15. 15.
    Wit, E., McClure, J.: Statistics for Microarrays: Design, Analysis and Inference. Wiley (2004)Google Scholar
  16. 16.
    Yang, Y.H., Dudoit, S., Luu, P., Lin, M.D., Peng, V., Ngai, J., Speed, T.P.: Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research 30 (2002)Google Scholar
  17. 17.
    Yuan, M., Kendziorski, C.: Hidden Markov Models for microarray time course data in multiple biological conditions. JASA 101, 1323–1340 (2006)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Claudia Angelini
    • 1
  • Daniela De Canditiis
    • 1
  • Marianna Pensky
    • 2
  • Naomi Brownstein
    • 3
  1. 1.Istituto per le Applicazioni del CalcoloCNRItaly
  2. 2.Department of MathematicsUniversity of Central FloridaUSA
  3. 3.Department of BiostatisticsUniversity of North Carolina at Chapel HillUSA

Personalised recommendations