Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes
<p>Abstract</p> <p>The ordinary-, penalized-, and bootstrap <it>t</it>-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), <it>i.e. </it>the fraction of falsely discovered genes, which was empirically...
| Main Authors: | , |
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| Format: | Article |
| Language: | deu |
| Published: |
BMC
2004-03-01
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| Series: | Genetics Selection Evolution |
| Subjects: | |
| Online Access: | http://www.gsejournal.org/content/36/2/191 |
| _version_ | 1811281231888777216 |
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| author | Goddard Mike E Meuwissen Theo HE |
| author_facet | Goddard Mike E Meuwissen Theo HE |
| author_sort | Goddard Mike E |
| collection | DOAJ |
| description | <p>Abstract</p> <p>The ordinary-, penalized-, and bootstrap <it>t</it>-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), <it>i.e. </it>the fraction of falsely discovered genes, which was empirically estimated in a duplicate of the data set. The bootstrap-<it>t</it>-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 <it>P</it>-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-<it>t</it>-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.</p> |
| first_indexed | 2024-04-13T01:30:04Z |
| format | Article |
| id | doaj.art-908c1c578c7c4679857bd4ec501a9091 |
| institution | Directory Open Access Journal |
| issn | 0999-193X 1297-9686 |
| language | deu |
| last_indexed | 2024-04-13T01:30:04Z |
| publishDate | 2004-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj.art-908c1c578c7c4679857bd4ec501a90912022-12-22T03:08:33ZdeuBMCGenetics Selection Evolution0999-193X1297-96862004-03-0136219120510.1186/1297-9686-36-2-191Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genesGoddard Mike EMeuwissen Theo HE<p>Abstract</p> <p>The ordinary-, penalized-, and bootstrap <it>t</it>-test, least squares and best linear unbiased prediction were compared for their false discovery rates (FDR), <it>i.e. </it>the fraction of falsely discovered genes, which was empirically estimated in a duplicate of the data set. The bootstrap-<it>t</it>-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 <it>P</it>-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-<it>t</it>-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.</p>http://www.gsejournal.org/content/36/2/191microarray datagene expressionnon-parametric bootstrapping<it>t</it>-testfalse discovery rates |
| spellingShingle | Goddard Mike E Meuwissen Theo HE Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes Genetics Selection Evolution microarray data gene expression non-parametric bootstrapping <it>t</it>-test false discovery rates |
| title | Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes |
| title_full | Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes |
| title_fullStr | Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes |
| title_full_unstemmed | Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes |
| title_short | Bootstrapping of gene-expression data improves and controls the false discovery rate of differentially expressed genes |
| title_sort | bootstrapping of gene expression data improves and controls the false discovery rate of differentially expressed genes |
| topic | microarray data gene expression non-parametric bootstrapping <it>t</it>-test false discovery rates |
| url | http://www.gsejournal.org/content/36/2/191 |
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