Bayesian Hierarchical Modeling and Selection of Differentially Expressed Genes for the EST Data

Fang Yu, Ming Hui Chen, Lynn Kuo, Peng Huang, Wanling Yang

Research output: Contribution to journalArticlepeer-review


Expressed sequence tag (EST) sequencing is a one-pass sequencing reading of cloned cDNAs derived from a certain tissue. The frequency of unique tags among different unbiased cDNA libraries is used to infer the relative expression level of each tag. In this article, we propose a hierarchical multinomial model with a nonlinear Dirichlet prior for the EST data with multiple libraries and multiple types of tissues. A novel hierarchical prior is developed and the properties of the proposed prior are examined. An efficient Markov chain Monte Carlo algorithm is developed for carrying out the posterior computation. We also propose a new selection criterion for detecting which genes are differentially expressed between two tissue types. Our new method with the new gene selection criterion is demonstrated via several simulations to have low false negative and false positive rates. A real EST data set is used to motivate and illustrate the proposed method.

Original languageEnglish (US)
Pages (from-to)142-150
Number of pages9
Issue number1
StatePublished - Mar 2011


  • Dirichlet distribution
  • Gene expression
  • Mixture distributions
  • Multinomial distribution
  • Shrinkage estimators

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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