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Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?

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  • Klebanov Lev

    (Department of Probability and Statistics, Charles University)

  • Yakovlev Andrei

    (University of Rochester, Rochester, NY)

Abstract

One of the prevailing ideas in the literature on microarray data analysis is to pool the expression measures across genes and treat them as a sample drawn from some distribution. Several universal laws were proposed to analytically describe this distribution. This idea raises a number of concerns. The expression levels of genes are not identically distributed random variables so that treating them as a sample amounts to sampling from a mixture of equally weighted distributions, each being associated with a different gene. The expression levels of different genes are heavily dependent random variables so that the law of large numbers and statistical goodness-of-fit tests are normally inapplicable to this kind of data. This dependence represents a very serious pitfall in microarray data analysis.

Suggested Citation

  • Klebanov Lev & Yakovlev Andrei, 2006. "Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-11, March.
    • Handle: RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:9
    DOI: 10.2202/1544-6115.1185
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    Cited by:

    1. Gordon, Alexander & Chen, Linlin & Glazko, Galina & Yakovlev, Andrei, 2009. "Balancing type one and two errors in multiple testing for differential expression of genes," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1622-1629, March.

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