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

J.M. Marín and M.T. Rodríguez-Bernal

Computational Statistics & Data Analysis, 2012, vol. 56, issue 6, 1898-1907

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.

Keywords: Clustering; MCMC computation; Microarray analysis; Mixture distributions; Multiple hypothesis testing; Non-central t-distribution (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:6:p:1898-1907

DOI: 10.1016/j.csda.2011.11.016

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