Paper Info
Title
Error-Pooling Empirical Bayes Model for Enhanced Statistical Discovery of Differential Expression in Microarray Data
Abstract
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.
Year
DOI
Venue
2008
10.1109/TSMCA.2007.914761
IEEE Transactions on Systems, Man, and Cybernetics, Part A
Keywords
Field
DocType
empirical bayes heterogeneous error model,error-pooling estimation,variance analysis,error estimation,pattern recognition,resampling-based estimation,biological errors,bayes methods,genetics,biological error,data analysis,replicated arrays,error pooling estimation,differential expression,significance analysis,statistical discovery,hem statistic,differential expression pattern,biology computing,microarray data,heteroscedastic error,differential expression patterns,monte carlo method,statistical approach,monte carlo methods,markov chain monte carlo (mcmc),gene expression data,markov chain,bayesian false discovery,error-pooling empirical bayes model,error statistics,enhanced statistical discovery,markov processes,empirical bayes (eb),technical errors,bayesian false discovery rate (fdr),gene expression,statistical significance,false discovery rate,analysis of variance,sample size,pattern analysis,markov chain monte carlo,bayesian methods,statistics
Data set,False discovery rate,Pattern recognition,Computer science,Pooling,Artificial intelligence,Prior probability,Resampling,Sample size determination,Bayesian probability,Bayes' theorem
Journal
Volume
Issue
ISSN
38
2
1083-4427
Citations 
PageRank 
References 
1
0.38
6
Authors
2
Name
Order
Citations
PageRank
Hyungjun Cho11048.44
J. K. Lee210.38