A constrained polynomial regression procedure for estimating the local False Discovery Rate
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
BMC Bioinformatics 2007, 8:229 doi:10.1186/1471-2105-8-229Published: 29 June 2007
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.
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.
A novel and efficient procedure for estimating lFDR was developed and evaluated.