By Topic

Error-Pooling Empirical Bayes Model for Enhanced Statistical Discovery of Differential Expression in Microarray Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
HyungJun Cho ; Korea Univ., Seoul ; Jae K. Lee

A number of statistical approaches have been proposed for evaluating the statistical significance of a differential expression in microarray data. The error estimation of these approaches is inaccurate when the number of replicated arrays is small. Consequently, their resulting statistics are often underpowered to detect important differential expression patterns in the microarray data with limited replication. In this paper, we propose an empirical Bayes (EB) heterogeneous error model (HEM) with error-pooling prior specifications for varying technical and biological errors in the microarray data. The error estimation of HEM is thus strengthened by and shrunk toward the EB priors that are obtained by the error-pooling estimation at each local intensity range. By using simulated and real data sets, we compared HEM with two widely used statistical approaches, significance analysis of microarray (SAM) and analysis of variance (ANOVA), to identify differential expression patterns across multiple conditions. The comparison showed that HEM is statistically more powerful than SAM and ANOVA, particularly when the sample size is smaller than five. We also suggest a resampling-based estimation of Bayesian false discovery rate to provide a biologically relevant cutoff criterion of HEM statistics.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans  (Volume:38 ,  Issue: 2 )