Ranking genes with respect to differential expression |
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Authors: | Broberg Per |
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Affiliation: | (1) Molecular Sciences, AstraZeneca R&D Lund, S-221 87 Lund, Sweden |
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Abstract: | Background In the pharmaceutical industry and in academia substantial efforts are made to make the best use of the promising microarray technology. The data generated by microarrays are more complex than most other biological data attracting much attention at this point. A method for finding an optimal test statistic with which to rank genes with respect to differential expression is outlined and tested. At the heart of the method lies an estimate of the false negative and false positive rates. Both investing in false positives and missing true positives lead to a waste of resources. The procedure sets out to minimise these errors. For calculation of the false positive and negative rates a simulation procedure is invoked. |
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