Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering |
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Authors: | Jung Yoon-Young Oh Man-Suk Shin Dong Wan Kang Seung-Ho Oh Hyun Sook |
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Institution: | Department of Statistics, Ewha Womans University, Seoul 120-750, Korea. |
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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. |
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Keywords: | Cluster analysis Hierarchical model False discovery rate Microarray data |
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