Open Access

Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes

Genetics Selection Evolution200436:191

Received: 17 December 2002

Accepted: 23 October 2003

Published: 15 March 2004


The ordinary-, penalized-, and bootstrap t-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), i.e. the fraction of falsely discovered genes, which was empirically estimated in a duplicate of the data set. The bootstrap-t-test yielded up to 80% lower FDRs than the alternative statistics, and its FDR was always as good as or better than any of the alternatives. Generally, the predicted FDR from the bootstrapped P-values agreed well with their empirical estimates, except when the number of mRNA samples is smaller than 16. In a cancer data set, the bootstrap-t-test discovered 200 differentially regulated genes at a FDR of 2.6%, and in a knock-out gene expression experiment 10 genes were discovered at a FDR of 3.2%. It is argued that, in the case of microarray data, control of the FDR takes sufficient account of the multiple testing, whilst being less stringent than Bonferoni-type multiple testing corrections. Extensions of the bootstrap simulations to more complicated test-statistics are discussed.


microarray data gene expression non-parametric bootstrapping t-test false discovery rates

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Authors’ Affiliations

Institute for Animal Science, Agricultural University of Norway
Institute of Land and Food Resources, University of Melbourne
Victorian Institute of Animal Science


© INRA, EDP Sciences 2004