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)


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.


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


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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

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