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Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

Title
Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering
Authors
Jung Y.-Y.Oh M.-S.Shin D.W.Kang S.-Ho.Oh H.S.
Ewha Authors
오만숙신동완강승호
SCOPUS Author ID
오만숙scopus; 신동완scopus
Issue Date
2006
Journal Title
Biometrical Journal
ISSN
0323-3847JCR Link
Citation
Biometrical Journal vol. 48, no. 3, pp. 435 - 450
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets. © 2006 WILEY-VCH Verlag GmbH & Co. KGaA.
DOI
10.1002/bimj.200410230
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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