### Abstract

In microarray data analysis, false discovery rate (FDR) is now widely accepted as the control criterion to account for multiple hypothesis testing. The proportion of equivalently expressed genes (π_{0}) is a key component to be estimated in the estimation of FDR. Some commonly used π_{0} estimators (BUM, SPLOSH, QVALUE, and LBE ) are all based on p-values, and they are essentially upper bounds of π_{0}. The simulations we carried out show that these four methods significantly overestimate the true π_{0} when differentially expressed genes and equivalently expressed genes are not well separated. To solve this problem, we first introduce a novel way of transforming the test statistics to make them symmetric about 0. Then we propose a π_{0} estimator based on the transformed test statistics using the symmetry assumption. Real data application and simulation both show that the π_{0} estimate from our method is less conservative than BUM, SPLOSH, QVALUE, and LBE in most of the cases. Simulation results also show that our estimator always has the least mean squared error among these five methods.

Original language | English (US) |
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Pages (from-to) | 177-187 |

Number of pages | 11 |

Journal | Journal of Computational Biology |

Volume | 17 |

Issue number | 2 |

DOIs | |

State | Published - Feb 1 2010 |

### Keywords

- Gene expression analysis
- Microarray
- Proportion of null hypothesis (π)
- Transformed test statistics

### ASJC Scopus subject areas

- Modeling and Simulation
- Molecular Biology
- Genetics
- Computational Mathematics
- Computational Theory and Mathematics

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## Cite this

*Journal of Computational Biology*,

*17*(2), 177-187. https://doi.org/10.1089/cmb.2009.0060