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A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments

Author

Listed:
  • Angelini Claudia

    (Istituto per le Applicazioni del Calcolo)

  • De Canditiis Daniela

    (Istituto per le Applicazioni del Calcolo)

  • Mutarelli Margherita

    (Lab. Bioinformatica, ISA-CNR; Dip. Patol. Gen., Seconda UniversitĪ¼a di Napoli, Italy)

  • Pensky Marianna

    (University of Central Florida)

Abstract

The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion over some orthonormal basis, where the coefficients and the number of basis functions are estimated from the data. The proposed procedure deals successfully with various technical difficulties that arise in typical microarray experiments such as a small number of observations, non-uniform sampling intervals and missing or replicated data. The procedure allows one to account for various types of errors and offers a good compromise between nonparametric techniques and techniques based on normality assumptions. In addition, all evaluations are performed using analytic expressions, so the entire procedure requires very small computational effort. The procedure is studied using both simulated and real data, and is compared with competitive recent approaches. Finally, the procedure is applied to a case study of a human breast cancer cell line stimulated with estrogen. We succeeded in finding new significant genes that were not marked in an earlier work on the same dataset.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sagmbi:v:6:y:2007:i:1:n:24
    DOI: 10.2202/1544-6115.1299
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    Cited by:

    1. 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.
    2. Vinciotti Veronica & Yu Keming, 2009. "M-quantile Regression Analysis of Temporal Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-22, September.
    3. Marot, Guillemette & Foulley, Jean-Louis & Jaffrzic, Florence, 2009. "A structural mixed model to shrink covariance matrices for time-course differential gene expression studies," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1630-1638, March.
    4. Donatello Telesca & Lurdes Y.T. Inoue & Mauricio Neira & Ruth Etzioni & Martin Gleave & Colleen Nelson, 2009. "Differential Expression and Network Inferences through Functional Data Modeling," Biometrics, The International Biometric Society, vol. 65(3), pages 793-804, September.

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