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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


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Volume 7, Issue 1

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Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study

David R. Bickel
  • Ottawa Institute of Systems Biology, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa
  • Other articles by this author:
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Published Online: 2008-03-10 | DOI: https://doi.org/10.2202/1544-6115.1330

The level of differential gene expression may be defined as a fold change, a frequency of upregulation, or some other measure of the degree or extent of a difference in expression across groups of interest. On the basis of expression data for hundreds or thousands of genes, inferring which genes are differentially expressed or ranking genes in order of priority introduces a bias in estimates of their differential expression levels. A previous correction of this feature selection bias suffers from a lack of generality in the method of ranking genes, from requiring many biological replicates, and from unnecessarily overcompensating for the bias.For any method of ranking genes on the basis of gene expression measured for as few as three biological replicates, a simple leave-one-out algorithm corrects, with less overcompensation, the bias in estimates of the level of differential gene expression. In a microarray data set, the bias correction reduces estimates of the probability of upregulation or downregulation from 100% to as low as 60%, even for genes with estimated local false discovery rates close to 0. A simulation study quantifies both the advantage of smoothing estimates of bias before correction and the degree of overcompensation.

Keywords: conditional bias; conditionally biased estimation; feature selection bias; shrinkage; empirical Bayes; gene rank; data resampling; transcriptional microarray; differential gene expression; fold change estimation; multiple comparisons; cross validation

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Published Online: 2008-03-10


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 7, Issue 1, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1330.

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[2]
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Canadian Journal of Statistics, 2011, Volume 39, Number 4, Page 610

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