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Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates

Author

Listed:
  • Lund Steven P.

    (Statistical Engineering Division, National Institute of Standards and Technology)

  • Nettleton Dan

    (Department of Statistics, Iowa State University)

  • McCarthy Davis J.

    (University of Oxford)

  • Smyth Gordon K.

    (Walter and Eliza Hall Institute of Medical Research)

Abstract

Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research. Here we present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth's (2004) approach to estimating gene-specific error variances for microarray data. Our suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data.

Suggested Citation

  • Lund Steven P. & Nettleton Dan & McCarthy Davis J. & Smyth Gordon K., 2012. "Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(5), pages 1-44, October.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:5:n:8
    DOI: 10.1515/1544-6115.1826
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    References listed on IDEAS

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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    2. Di Yanming & Schafer Daniel W & Cumbie Jason S & Chang Jeff H, 2011. "The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-28, May.
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