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Advanced Statistical Methods for the Analysis of Large Data-Sets pp 47–56Cite as

Bayesian Methods for Time Course Microarray Analysis: From Genes’ Detection to Clustering

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Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

Abstract

Time-course microarray experiments are an increasingly popular approach for understanding the dynamical behavior of a wide range of biological systems. In this paper we discuss some recently developed functional Bayesian methods specifically designed for time-course microarray data. The methods allow one to identify differentially expressed genes, to rank them, to estimate their expression profiles and to cluster the genes associated with the treatment according to their behavior across time. The methods successfully deal with various technical difficulties that arise in this type of experiments such as a large number of genes, a small number of observations, non-uniform sampling intervals, missing or multiple data and temporal dependence between observations for each gene. The procedures are illustrated using both simulated and real data.

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Acknowledgements

This research was supported in part by the CNR-Bioinformatics projects, the CNR DG.RSTL.004.002 Project. Marianna Pensky was supported in part by National Science Foundation (NSF), grant DMS-1106564 and the CNR Short term mobility grant 2008.

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Authors and Affiliations

  1. Istituto per le Applicazioni del Calcolo, CNR, Rome, Italy

    Claudia Angelini & Daniela De Canditiis

  2. Department of Mathematics, University of Central Florida, Orlando, USA

    Marianna Pensky

Authors
  1. Claudia Angelini
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  2. Daniela De Canditiis
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  3. Marianna Pensky
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Corresponding author

Correspondence to Claudia Angelini .

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Editors and Affiliations

  1. , Dept. of Statistics, University of Roma "La Sapienza", P.le Aldo Moro 5, Roma, 00185, Italy

    Agostino Di Ciaccio

  2. , Dip. di Metodi Quantitativi e Teoria Eco, University "G. d'Annunzio" of Chieti-Pes, V.le Pindaro 42, Pescara, Italy

    Mauro Coli

  3. Fac. Ciencias, Depto. Estadística e Investigación Opera, Universidad de Granada, Avenida Fuente Nueva s/n, Granada, 18071, Spain

    Jose Miguel Angulo Ibanez

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Angelini, C., De Canditiis, D., Pensky, M. (2012). Bayesian Methods for Time Course Microarray Analysis: From Genes’ Detection to Clustering. In: Di Ciaccio, A., Coli, M., Angulo Ibanez, J. (eds) Advanced Statistical Methods for the Analysis of Large Data-Sets. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21037-2_5

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