Estimating the proportion of equivalently expressed genes in microarray data based on transformed test statistics

Shuo Jiao, Shunpu Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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 (π0) is a key component to be estimated in the estimation of FDR. Some commonly used π0 estimators (BUM, SPLOSH, QVALUE, and LBE ) are all based on p-values, and they are essentially upper bounds of π0. The simulations we carried out show that these four methods significantly overestimate the true π0 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 π0 estimator based on the transformed test statistics using the symmetry assumption. Real data application and simulation both show that the π0 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 languageEnglish (US)
Pages (from-to)177-187
Number of pages11
JournalJournal of Computational Biology
Issue number2
StatePublished - Feb 1 2010


  • 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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