### Abstract

Given a set of microarray data, the problem is to detect differentially expressed genes, using a false discovery rate (FDR) criterion. As opposed to common procedures in the literature, we do not base the selection criterion on statistical significance only, but also on the effect size. Therefore, we select only those genes that are significantly more differentially expressed than some f-fold (e.g., f = 2). This corresponds to use of an interval null domain for the effect size. Based on a simple error model, we discuss a naive estimator for the FDR, interpreted as the probability that the parameter of interest lies in the null-domain (e.g., μ < log

Original language | English |
---|---|

Pages (from-to) | 806-815 |

Journal | Biometrics |

Volume | 63 |

Issue number | 3 |

DOIs | |

Publication status | Published - 2007 |

## Fingerprint Dive into the research topics of 'Estimating the false discovery rate using nonparametric deconvolution'. Together they form a unique fingerprint.

## Cite this

van de Wiel, M. A., & Kim, K. I. (2007). Estimating the false discovery rate using nonparametric deconvolution.

*Biometrics*,*63*(3), 806-815. https://doi.org/10.1111/j.1541-0420.2006.00736.x