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

Yoon Young Jung, Man Suk Oh, Dong Wan Shin, Seung Ho Kang, Hyun Sook Oh

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish
Pages (from-to)435-450
Number of pages16
JournalBiometrical Journal
Issue number3
StatePublished - Jun 2006


  • Cluster analysis
  • False discovery rate
  • Hierarchical model
  • Microarray data


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