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A model selection criterion for model-based clustering of annotated gene expression data

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  • Gallopin Mélina
  • Celeux Gilles

    (Inria Saclay Île-de-France, Université Paris-Sud, 91405, Orsay Cedex, France)

  • Jaffrézic Florence
  • Rau Andrea

Abstract

In co-expression analyses of gene expression data, it is often of interest to interpret clusters of co-expressed genes with respect to a set of external information, such as a potentially incomplete list of functional properties for which a subset of genes may be annotated. Based on the framework of finite mixture models, we propose a model selection criterion that takes into account such external gene annotations, providing an efficient tool for selecting a relevant number of clusters and clustering model. This criterion, called the integrated completed annotated likelihood (ICAL), is defined by adding an entropy term to a penalized likelihood to measure the concordance between a clustering partition and the external annotation information. The ICAL leads to the choice of a model that is more easily interpretable with respect to the known functional gene annotations. We illustrate the interest of this model selection criterion in conjunction with Gaussian mixture models on simulated gene expression data and on real RNA-seq data.

Suggested Citation

  • Gallopin Mélina & Celeux Gilles & Jaffrézic Florence & Rau Andrea, 2015. "A model selection criterion for model-based clustering of annotated gene expression data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(5), pages 413-428, November.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:5:p:413-428:n:5
    DOI: 10.1515/sagmb-2014-0095
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    References listed on IDEAS

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    1. Ian Holmes & Keith Harris & Christopher Quince, 2012. "Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-15, February.
    2. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard & Langrognet, Florent, 2006. "Model-based cluster and discriminant analysis with the MIXMOD software," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 587-600, November.
    3. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    4. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    5. Lebret, Rémi & Iovleff, Serge & Langrognet, Florent & Biernacki, Christophe & Celeux, Gilles & Govaert, Gérard, 2015. "Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i06).
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