Economics at your fingertips  

The NBP Negative Binomial Model for Assessing Differential Gene Expression from RNA-Seq

Di Yanming, Schafer Daniel W, Cumbie Jason S and Chang Jeff H
Additional contact information
Di Yanming: Oregon State University
Schafer Daniel W: Oregon State University
Cumbie Jason S: Oregon State University
Chang Jeff H: Oregon State University

Statistical Applications in Genetics and Molecular Biology, 2011, vol. 10, issue 1, 1-28

Abstract: We propose a new statistical test for assessing differential gene expression using RNA sequencing (RNA-Seq) data. Commonly used probability distributions, such as binomial or Poisson, cannot appropriately model the count variability in RNA-Seq data due to overdispersion. The small sample size that is typical in this type of data also prevents the uncritical use of tools derived from large-sample asymptotic theory. The test we propose is based on the NBP parameterization of the negative binomial distribution. It extends an exact test proposed by Robinson and Smyth (2007, 2008). In one version of Robinson and Smyth’s test, a constant dispersion parameter is used to model the count variability between biological replicates. We introduce an additional parameter to allow the dispersion parameter to depend on the mean. Our parametric method complements nonparametric regression approaches for modeling the dispersion parameter. We apply the test we propose to an Arabidopsis data set and a range of simulated data sets. The results show that the test is simple, powerful and reasonably robust against departures from model assumptions.

Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link) ... .1637.xml?format=INT (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:

Ordering information: This journal article can be ordered from

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
Series data maintained by Peter Golla ().

Page updated 2017-09-29
Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:24