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The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

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  • Di Yanming
  • Schafer Daniel W
  • Cumbie Jason S
  • Chang Jeff H

Abstract

We propose a new statistical test for assessing differential gene expression using RNA sequencing (RNA-Seq) data. Commonly used probability distributions, such as binomial or Poisson, cannot appropriately model the count variability in RNA-Seq data due to overdispersion. The small sample size that is typical in this type of data also prevents the uncritical use of tools derived from large-sample asymptotic theory. The test we propose is based on the NBP parameterization of the negative binomial distribution. It extends an exact test proposed by Robinson and Smyth (2007, 2008). In one version of Robinson and Smyth’s test, a constant dispersion parameter is used to model the count variability between biological replicates. We introduce an additional parameter to allow the dispersion parameter to depend on the mean. Our parametric method complements nonparametric regression approaches for modeling the dispersion parameter. We apply the test we propose to an Arabidopsis data set and a range of simulated data sets. The results show that the test is simple, powerful and reasonably robust against departures from model assumptions.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:24
    DOI: 10.2202/1544-6115.1637
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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.
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    Cited by:

    1. Di Yanming & Emerson Sarah C. & Schafer Daniel W. & Kimbrel Jeffrey A. & Chang Jeff H., 2013. "Higher order asymptotics for negative binomial regression inferences from RNA-sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(1), pages 49-70, March.
    2. Gu Mi & Yanming Di, 2015. "The Level of Residual Dispersion Variation and the Power of Differential Expression Tests for RNA-Seq Data," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-25, April.
    3. Gu Mi & Yanming Di & Sarah Emerson & Jason S Cumbie & Jeff H Chang, 2012. "Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-10, October.
    4. Chen, Xiongzhi, 2019. "Uniformly consistently estimating the proportion of false null hypotheses via Lebesgue–Stieltjes integral equations," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 724-744.
    5. 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.
    6. Jungsoo Gim & Sungho Won & Taesung Park, 2016. "LPEseq: Local-Pooled-Error Test for RNA Sequencing Experiments with a Small Number of Replicates," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-15, August.
    7. Kotoka Ekua & Orr Megan, 2017. "Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(5-6), pages 333-347, December.
    8. Gu Mi & Yanming Di & Daniel W Schafer, 2015. "Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-16, March.

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