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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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  • Marín, J.M.
  • Rodríguez-Bernal, M.T.

Abstract

Multiple testing analysis and clustering methodologies are usually applied in microarray data analysis. A combination of both methods to deal with multiple comparisons among groups obtained from microarray expressions of genes is proposed. Assuming normal data, a statistic which depends on sample means and sample variances, distributed as a non-central t-distribution is defined. As multiple comparisons among groups are considered, 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.

Suggested Citation

  • Marín, J.M. & Rodríguez-Bernal, M.T., 2012. "Multiple hypothesis testing and clustering with mixtures of non-central t-distributions applied in microarray data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1898-1907.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1898-1907
    DOI: 10.1016/j.csda.2011.11.016
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    References listed on IDEAS

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