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Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome

Author

Listed:
  • Bérard Caroline

    (UMR AgroParisTech/INRA MIA 518)

  • Martin-Magniette Marie-Laure

    (UMR AgroParisTech/INRA MIA 518, URGV UMR INRA/CNRS/UEVE)

  • Brunaud Véronique

    (URGV UMR INRA/CNRS/UEVE)

  • Aubourg Sébastien

    (URGV UMR INRA/CNRS/UEVE)

  • Robin Stéphane

    (UMR AgroParisTech/INRA MIA 518)

Abstract

Tiling arrays make possible a large-scale exploration of the genome thanks to probes which cover the whole genome with very high density, up to 2,000,000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work, we propose to consider both questions simultaneously as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge such as annotation and spatial dependence between probes. Since probes are not biologically relevant units, we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and of the region classification. The "TAHMMAnnot" package is implemented in R and C and is freely available from CRAN.

Suggested Citation

  • Bérard Caroline & Martin-Magniette Marie-Laure & Brunaud Véronique & Aubourg Sébastien & Robin Stéphane, 2011. "Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, November.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:50
    DOI: 10.2202/1544-6115.1692
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    References listed on IDEAS

    as
    1. Fridlyand, Jane & Snijders, Antoine M. & Pinkel, Dan & Albertson, Donna G. & Jain, A.N.Ajay N., 2004. "Hidden Markov models approach to the analysis of array CGH data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 132-153, July.
    2. Gilles Celeux & Jean-Baptiste Durand, 2008. "Selecting hidden Markov model state number with cross-validated likelihood," Computational Statistics, Springer, vol. 23(4), pages 541-564, October.
    3. Sunduz Keles & Mark van der Laan & Sandrine Dudoit & Simon Cawley, 2004. "Multiple Testing Methods For ChIP-Chip High Density Oligonucleotide Array Data," U.C. Berkeley Division of Biostatistics Working Paper Series 1147, Berkeley Electronic Press.
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    Cited by:

    1. Volant, Stevenn & Martin Magniette, Marie-Laure & Robin, Stéphane, 2012. "Variational Bayes approach for model aggregation in unsupervised classification with Markovian dependency," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2375-2387.

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