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Empirical Bayes Microarray ANOVA and Grouping Cell Lines by Equal Expression Levels

Author

Listed:
  • Lönnstedt Ingrid

    (Uppsala University)

  • Rimini Rebecca

    (Department of Biotechnology, KTH - Royal Institute of Technology)

  • Nilsson Peter

    (Department of Biotechnology, KTH - Royal Institute of Technology)

Abstract

In the exploding field of gene expression techniques such as DNA microarrays, there are still few general probabilistic methods for analysis of variance. Linear models and ANOVA are heavily used tools in many other disciplines of scientific research. The usual F-statistic is unsatisfactory for microarray data, which explore many thousand genes in parallel, with few replicates.We present three potential one-way ANOVA statistics in a parametric statistical framework. The aim is to separate genes that are differently regulated across several treatment conditions from those with equal regulation. The statistics have different features and are evaluated using both real and simulated data. Our statistic B1 generally shows the best performance, and is extended for use in an algorithm that groups cell lines by equal expression levels for each gene. An extension is also outlined for more general ANOVA tests including several factors.The methods presented are implemented in the freely available statistical language R. They are available at http://www.math.uu.se/staff/pages/?uname=ingrid.

Suggested Citation

  • Lönnstedt Ingrid & Rimini Rebecca & Nilsson Peter, 2005. "Empirical Bayes Microarray ANOVA and Grouping Cell Lines by Equal Expression Levels," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-34, April.
    • Handle: RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:7
    DOI: 10.2202/1544-6115.1125
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    Cited by:

    1. Yang, Tae Young, 2009. "Efficient multi-class cancer diagnosis algorithm, using a global similarity pattern," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 756-765, January.

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