Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates

Stat Appl Genet Mol Biol. 2012 Oct 22;11(5):/j/sagmb.2012.11.issue-5/1544-6115.1826/1544-6115.1826.xml. doi: 10.1515/1544-6115.1826.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Base Sequence
  • Databases, Genetic
  • Gene Expression Profiling / methods
  • Gene Expression Profiling / statistics & numerical data*
  • Likelihood Functions
  • RNA, Messenger / metabolism
  • Sequence Analysis, RNA / methods*


  • RNA, Messenger