Statistical methods for overdispersion in mRNA-seq count data

Hui Zhang*, Stanley B. Pounds, Li Tang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Recent developments in Next-Generation Sequencing (NGS) technologies have opened doors for ultra high throughput sequencing mRNA (mRNA-seq) of the whole transcriptome. mRNA-seq has enabled researchers to comprehensively search for underlying biological determinants of diseases and ultimately discover novel preventive and therapeutic solutions. Unfortunately, given the complexity of mRNA-seq data, data generation has outgrown current analytical capacity, hindering the pace of research in this area. Thus, there is an urgent need to develop novel statistical methodology that addresses problems related to mRNA-seq data. This review addresses the common challenge of the presence of overdispersion in mRNA count data. We review current methods for modeling overdispersion, such as negative binomial, quasi-likelihood Poisson method, and the two-stage adaptive method; introduce related statistical theories; and discuss their applications to mRNA-seq count data.

Original languageEnglish (US)
Pages (from-to)34-40
Number of pages7
JournalOpen Bioinformatics Journal
Volume7
Issue numberSUPPL.1
DOIs
StatePublished - 2013

Keywords

  • Count response
  • Negative binomial theory
  • Over-dispersion
  • Poisson
  • Quasi-likelihood
  • mRNA-seq

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Biomedical Engineering
  • Health Informatics

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