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

publication date

  • June 2012

start page

  • 1898

end page

  • 1907

issue

  • 6

volume

  • 56

international standard serial number (ISSN)

  • 0167-9473

electronic international standard serial number (EISSN)

  • 1872-7352

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