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

Lund Steven P., Nettleton Dan, McCarthy Davis J. and Smyth Gordon K.
Additional contact information
Lund Steven P.: Statistical Engineering Division, National Institute of Standards and Technology
Nettleton Dan: Department of Statistics, Iowa State University
McCarthy Davis J.: University of Oxford
Smyth Gordon K.: Walter and Eliza Hall Institute of Medical Research

Statistical Applications in Genetics and Molecular Biology, 2012, vol. 11, issue 5, 44

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 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/1544-6115.1826 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:11:y:2012:i:5:n:8

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/1544-6115.1826

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().