A constrained polynomial regression procedure for estimating the local False Discovery Rate

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• 作者：Cyril Dalmasso (1)
  Avner Bar-Hen (2)
  Philippe Bro?t (1)
  
• 刊名：BMC Bioinformatics
• 出版年：2007
• 出版时间：December 2007
• 年：2007
• 卷：8
• 期：1
• 全文大小：786KB
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• 作者单位：Cyril Dalmasso (1)
  Avner Bar-Hen (2)
  Philippe Bro?t (1)
  
  1. JE 2492 -Univ. Paris-Sud, 16 avenue Paul Vaillant Couturier, F94807, Villejuif, France
  2. UMR AgroParisTech/INRA 558, 16 rue Claude Bernard, 75231, Paris, France
  
• ISSN：1471-2105

文摘

Background In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR), which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate (FDR) for subsets of genes. Results In the framework of estimating procedures without any distributional assumption under the alternative hypothesis, a new and efficient procedure for estimating the lFDR is described. The results of a simulation study indicated good performances for the proposed estimator in comparison to four published ones. The five different procedures were applied to real datasets. Conclusion A novel and efficient procedure for estimating lFDR was developed and evaluated.