Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

2012

Journal or Book Title

Statistical Applications in Genetics and Molecular Biology

Volume

11

Issue

5

First Page

8

DOI

10.1515/1544-6115.1826

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.

Rights

Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.

Language

en

File Format

application/pdf

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