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

Genetics Selection Evolution

Mike E Goddard¹  Theo HE Meuwissen² 

[1] Institute of Land and Food Resources, University of Melbourne, Parkville, 3052 Australia, and Victorian Institute of Animal Science, Attwood, Victoria, 3049 Australia;Institute for Animal Science, Agricultural University of Norway, 1432 Ås, Norway

关键词: false discovery rates;    t-test;    non-parametric bootstrapping;    gene expression;    microarray data;   

Others  :  1094341
DOI  :  10.1186/1297-9686-36-2-191

 received in 2002-12-17, accepted in 2003-10-23,  发布年份 2004

【 摘 要 】

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.

【 授权许可】

   
2004 INRA, EDP Sciences