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Bayesian models for two-sample time-course microarray experiments

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  • Angelini, Claudia
  • De Canditiis, Daniela
  • Pensky, Marianna

Abstract

A truly functional Bayesian method for detecting temporally differentially expressed genes between two experimental conditions is presented. The method distinguishes between two biologically different set ups, one in which the two samples are interchangeable, and one in which the second sample is a modification of the first, i.e.the two samples are non-interchangeable. This distinction leads to two different Bayesian models, which allow more flexibility in modeling gene expression profiles. The method allows one to identify differentially expressed genes, to rank them and to estimate their expression profiles. The proposed procedure successfully deals with various technical difficulties which arise in microarray time-course experiments, such as small number of observations, non-uniform sampling intervals and presence of missing data or repeated measurements. The procedure allows one to account for various types of error, 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 little computational effort. The performance of the procedure is studied using simulated and real data.

Suggested Citation

  • Angelini, Claudia & De Canditiis, Daniela & Pensky, Marianna, 2009. "Bayesian models for two-sample time-course microarray experiments," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1547-1565, March.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1547-1565
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    References listed on IDEAS

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    1. Angelini Claudia & De Canditiis Daniela & Mutarelli Margherita & Pensky Marianna, 2007. "A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 6(1), pages 1-33, September.
    2. Laure Ambroise & Jean-Marc Ferrandi & Dwight Merunka & Pierre Valette-Florence, 2004. "How well does brand personality predict brand choice ?," Post-Print halshs-00525048, HAL.
    3. Heard, Nicholas A. & Holmes, Christopher C. & Stephens, David A., 2006. "A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes: An Application of Bayesian Hierarchical Clustering of Curves," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 18-29, March.
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    1. Claudia Angelini & Daniela De Canditiis & Marianna Pensky, 2012. "Clustering time-course microarray data using functional Bayesian infinite mixture model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 129-149, March.

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