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A mixture model approach for the analysis of small exploratory microarray experiments

Author

Listed:
  • Muir, W.M.
  • Rosa, G.J.M.
  • Pittendrigh, B.R.
  • Xu, Z.
  • Rider, S.D.
  • Fountain, M.
  • Ogas, J.

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.

Suggested Citation

  • Muir, W.M. & Rosa, G.J.M. & Pittendrigh, B.R. & Xu, Z. & Rider, S.D. & Fountain, M. & Ogas, J., 2009. "A mixture model approach for the analysis of small exploratory microarray experiments," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1566-1576, March.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1566-1576
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    References listed on IDEAS

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    1. Allison, David B. & Gadbury, Gary L. & Heo, Moonseong & Fernandez, Jose R. & Lee, Cheol-Koo & Prolla, Tomas A. & Weindruch, Richard, 2002. "A mixture model approach for the analysis of microarray gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 39(1), pages 1-20, March.
    2. Kim‐Anh Do & Peter Müller & Feng Tang, 2005. "A Bayesian mixture model for differential gene expression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 627-644, June.
    3. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205, February.
    4. Göran Kauermann & Paul Eilers, 2004. "Modeling Microarray Data Using a Threshold Mixture Model," Biometrics, The International Biometric Society, vol. 60(2), pages 376-387, June.
    5. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
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