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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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

Steven P. Lund1 / Dan Nettleton2 / Davis J. McCarthy3 / Gordon K. Smyth4

1Statistical Engineering Division, National Institute of Standards and Technology

2Department of Statistics, Iowa State University

3University of Oxford

4Walter and Eliza Hall Institute of Medical Research

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 11, Issue 5, ISSN (Online) 1544-6115, DOI: 10.1515/1544-6115.1826, October 2012

Publication History

Published Online:
2012-10-22

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.

Keywords: differential expression; quasi-likelihood; RNA-seq

Citing Articles

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[3]
Chen Chu, Zhaoben Fang, Xing Hua, Yaning Yang, Enguo Chen, Allen W. Cowley, Mingyu Liang, Pengyuan Liu, and Yan Lu
BMC Genomics, 2015, Volume 16, Number 1
[4]
Conrad J. Burden, Sumaira E. Qureshi, and Susan R. Wilson
PeerJ, 2014, Volume 2, Page e576

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