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

Date
2012-01-01
Authors
Lund, Steven
Nettleton, Dan
Nettleton, Dan
McCarthy, Davis
Smyth, Gordon
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Department
Statistics
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.

Comments

This article is published as Lund, Steven P., Dan Nettleton, Davis J. McCarthy, and Gordon K. Smyth. "Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates." Statistical applications in genetics and molecular biology 11, no. 5 (2012): 8. doi: 10.1515/1544-6115.1826.

Description
Keywords
Citation
DOI
Collections