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

Authors:
J. M. Marín;M. T. Rodríguez-Bernal
Affiliations:
Dep. Estadística, U. Carlos III, 28903 Getafe, Spain;Dep. Estadística e I.O., Fac. Matemáticas, U. Complutense, 28040 Madrid, Spain
Venue:
Computational Statistics & Data Analysis
Year:
2012

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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.