Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Medicine and Health Sciences
A Mixture Model Based Approach For Estimating The Fdr In Replicated Microarray Data, Shuo Jiao, Shunpu Zhang
A Mixture Model Based Approach For Estimating The Fdr In Replicated Microarray Data, Shuo Jiao, Shunpu Zhang
Shuo Jiao
One of the mostly used methods for estimating the false discovery rate (FDR) is the permutation based method. The permutation based method has the well-known granularity problem due to the discrete nature of the permuted null scores. The granularity problem may produce very unstable FDR estimates. Such instability may cause scientists to over- or under-estimate the number of false positives among the genes declared as significant, and hence result in inaccurate interpretation of biological data. In this paper, we propose a new model based method as an improvement of the permutation based FDR estimation method of SAM [1] The new …
Estimating The Proportion Of Equivalently Expressed Genes In Microarray Data Based On Transformed Test Statistics, Shuo Jiao, Shunpu Zhang
Estimating The Proportion Of Equivalently Expressed Genes In Microarray Data Based On Transformed Test Statistics, Shuo Jiao, Shunpu Zhang
Shuo Jiao
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 …