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iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data

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Statistical Analysis for High-Dimensional Data

Part of the book series: Abel Symposia ((ABEL,volume 11))

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Abstract

We describe a method for the integration of high-throughput data from different sources. More specifically, iBATCGH is a package for the integrative analysis of transcriptomic and genomic data, based on a hierarchical Bayesian model. Through the specification of a measurement error model we relate the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurement via a hidden Markov model. Selection of relevant associations is performed employing variable selection priors that explicitly incorporate dependence information across adjacent copy number states. Posterior inference is carried out through Markov chain Monte Carlo techniques that efficiently explores the space of all possible associations. In this chapter we review the model and present the functions provided in iBATCGH, an R package based on a C implementation of the inferential algorithm. Lastly, we illustrate the method via a case study on ovarian cancer.

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Author information

Authors and Affiliations

  1. Maastricht University, Maastricht, The Netherlands

    Alberto Cassese

  2. UT MD Anderson Cancer Center, Houston, TX, USA

    Michele Guindani

  3. Department of Statistics, Rice University, Houston, TX, USA

    Marina Vannucci

Authors
  1. Alberto Cassese
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  2. Michele Guindani
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  3. Marina Vannucci
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Corresponding author

Correspondence to Marina Vannucci .

Editor information

Editors and Affiliations

  1. Oslo Centre for Biostatistics and Epide, University of Oslo, Oslo, Norway

    Arnoldo Frigessi

  2. Seminar for Statistics, ETH Zürich, Zürich, Switzerland

    Peter Bühlmann

  3. Department of Mathematics, University of Oslo, Oslo, Norway

    Ingrid K. Glad

  4. Norwegian University of Science and Tec, Department of Mathematical Sciences, Trondheim, Norway

    Mette Langaas

  5. University of Cambridge, MRC Biostatistics Unit, Cambridge Instit, Cambridge, United Kingdom

    Sylvia Richardson

  6. Department of Statistics, Rice University, Houston, Texas, USA

    Marina Vannucci

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Cassese, A., Guindani, M., Vannucci, M. (2016). iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH Data. In: Frigessi, A., Bühlmann, P., Glad, I., Langaas, M., Richardson, S., Vannucci, M. (eds) Statistical Analysis for High-Dimensional Data. Abel Symposia, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-319-27099-9_6

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