Dataset Information
A constrained polynomial regression procedure for estimating the local False Discovery Rate.
Ontology highlight
ABSTRACT: 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.
SUBMITTER: Dalmasso C
PROVIDER: S-EPMC1940264 | biostudies-literature | 2007 Jun
REPOSITORIES: biostudies-literature
Publications
A constrained polynomial regression procedure for estimating the local False Discovery Rate.
BMC bioinformatics 20070629
<h4>Background</h4>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-Hoc ...[more]