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Multiple hypothesis testing and clustering with mixtures of non-central t-distributions applied in microarray data analysis

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  • Miguel J. Marín

    ()

  • M. Teresa Rodríguez-Bernal

    ()

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    Abstract

    Multiple testing analysis, based on clustering methodologies, is usually applied in Microarray Data Analysis for comparisons between pair of groups. In this paper, we generalize this methodology to deal with multiple comparisons among more than two groups obtained from microarray expressions of genes. Assuming normal data, we define a statistic which depends on sample means and sample variances, distributed as a non-central t-distribution. As we consider multiple comparisons among groups, a mixture of non-central t-distributions is derived. The estimation of the components of mixtures is obtained via a Bayesian approach, and the model is applied in a multiple comparison problem from a microarray experiment obtained from gorilla, bonobo and human cultured fibroblasts.

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    File URL: http://e-archivo.uc3m.es/bitstream/10016/9604/1/ws104427.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws104427.

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    Date of creation: Nov 2010
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    Handle: RePEc:cte:wsrepe:ws104427

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    Related research

    Keywords: Clustering; MCMC computation; Microarray analysis; Mixture distributions; Multiple hypothesis testing; Non-central t-distribution;

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    1. S. Choy & A. Smith, 1997. "Hierarchical models with scale mixtures of normal distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 6(1), pages 205-221, June.
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