A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data

Hao Wu, Chi Wang, Zhijin Wu

Research output: Contribution to journalArticlepeer-review

132 Scopus citations

Abstract

Recent developments in RNA-sequencing (RNA-seq) technology have led to a rapid increase in gene expression data in the form of counts. RNA-seq can be used for a variety of applications, however, identifying differential expression (DE) remains a key task in functional genomics. There have been a number of statistical methods for DE detection for RNA-seq data. One common feature of several leading methods is the use of the negative binomial (Gamma-Poisson mixture) model. That is, the unobserved gene expression is modeled by a gamma random variable and, given the expression, the sequencing read counts are modeled as Poisson. The distinct feature in various methods is how the variance, or dispersion, in the Gamma distribution is modeled and estimated. We evaluate several large public RNA-seq datasets and find that the estimated dispersion in existing methods does not adequately capture the heterogeneity of biological variance among samples. We present a new empirical Bayes shrinkage estimate of the dispersion parameters and demonstrate improved DE detection.

Original languageEnglish
Pages (from-to)232-243
Number of pages12
JournalBiostatistics
Volume14
Issue number2
DOIs
StatePublished - Apr 2013

Keywords

  • Differential expression
  • Empirical Bayes
  • RNA sequencing
  • Shrinkage estimator

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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