Detecting DE in RNA-Seq Data Using Quasi-likelihood with Shrunken Dispersion Estimates

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, researchers at National Institute of Standards and Technology 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. The 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.

An R package called QuasiSeq, used to implement the suggested methods of this article is available from the CRAN website:

  • Lund SP, Nettleton D, McCarthy DJ, Smyth GK. (2012) Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates. Stat Appl Genet Mol Biol. 11(5). [article]
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