Empirical Bayes analysis of unreplicated microarray data

Hyung Jun Cho, Jaewoo Kang, Jae K. Lee

    Research output: Contribution to journalArticlepeer-review


    Because of the high costs of microarray experiments and the availability of only limited biological materials, microarray experiments are often performed with a small number of replicates. Investigators, therefore, often have to perform their experiments with low replication or without replication. However, the heterogeneous error variability observed in microarray experiments increases the difficulty in analyzing microarray data without replication. No current analysis techniques are practically applicable to such microarray data analysis. We here introduce a statistical method, the so-called unreplicated heterogeneous error model (UHEM) for the microarray data analysis without replication. This method is possible by utilizing many adjacent-intensity genes for estimating local error variance after nonparametric elimination of differentially expressed genes between different biological conditions. We compared the performance of UHEM with three empirical Bayes prior specification methods: between-condition local pooled error, pseudo standard error, or adaptive standard error-based HEM. We found that our unreplicated HEM method is effective for the microarray data analysis when replication of an array experiment is impractical or prohibited.

    Original languageEnglish
    Pages (from-to)393-408
    Number of pages16
    JournalComputational Statistics
    Issue number3
    Publication statusPublished - 2009


    • Empirical Bayes
    • Markov chain Monte Carlo
    • Microarray data
    • No replication

    ASJC Scopus subject areas

    • Statistics and Probability
    • Statistics, Probability and Uncertainty
    • Computational Mathematics


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