 A mixture model approach for the analysis of small exploratory microarray experimentsW.M. Muir, G.J.M. Rosa, B.R. Pittendrigh, Z. Xu, S.D. Rider, M. Fountain and J. Ogas Computational Statistics & Data Analysis, 2009, vol. 53, issue 5, 1566-1576 Abstract: The microarray is an important and powerful tool for prescreening of genes for further research. However, alternative solutions are needed to increase power in small microarray experiments. Use of traditional parametric and even non-parametric tests for such small experiments lack power and have distributional problems. A mixture model is described that is performed directly on expression differences assuming that genes in alternative treatments are expressed or not in all combinations (i) not expressed in either condition, (ii) expressed only under the first condition, (iii) expressed only under the second condition, and (iv) expressed under both conditions, giving rise to 4 possible clusters with two treatments. The approach is termed a Mean-Difference-Mixture-Model (MD-MM) method. Accuracy and power of the MD-MM was compared to other commonly used methods, using both simulations, microarray data, and quantitative real time PCR (qRT-PCR). The MD-MM was found to be generally superior to other methods in most situations. The advantage was greatest in situations where there were few replicates, poor signal to noise ratios, or non-homogeneous variances. Date: 2009 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:5:p:1566-1576 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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