Estimating the Proportion of Equivalently Expressed Genes in Microarray Data Based on Transformed Test Statistics

Abstract

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 (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.