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Error-pooling empirical Bayes model for enhanced statistical discovery of differential expression in microarray data

Authors
Cho, HyungJunLee, Jae K.
Issue Date
3월-2008
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Bayesian false discovery rate (FDR); empirical Bayes (EB); gene expression data; heteroscedastic error; Markov chain Monte Carlo (MCMC)
Citation
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS, v.38, no.2, pp.425 - 436
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS
Volume
38
Number
2
Start Page
425
End Page
436
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/123933
DOI
10.1109/TSMCA.2007.914761
ISSN
1083-4427
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.
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CHO, HYUNG JUN
정경대학 (통계학과)
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