On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples
We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature.
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Volume (Year): 90 (2004)
Issue (Month): 1 (July)
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- Huber Wolfgang & von Heydebreck Anja & Sueltmann Holger & Poustka Annemarie & Vingron Martin, 2003. "Parameter estimation for the calibration and variance stabilization of microarray data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 2(1), pages 1-24, April.
- Coleman, Dan & Dong, Xioapeng & Hardin, Johanna & Rocke, David M. & Woodruff, David L., 1999. "Some computational issues in cluster analysis with no a priori metric," Computational Statistics & Data Analysis, Elsevier, vol. 31(1), pages 1-11, July.
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