Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Sanguinetti, Guido
IMPACT FACTOR 2017: 0.812
5-year IMPACT FACTOR: 1.104
CiteScore 2017: 0.86
SCImago Journal Rank (SJR) 2017: 0.456
Source Normalized Impact per Paper (SNIP) 2017: 0.527
Mathematical Citation Quotient (MCQ) 2017: 0.04
A Two-Stage Poisson Model for Testing RNA-Seq Data
RNA sequencing technology is providing data of unprecedented throughput, resolution, and accuracy. Although there are many different computational tools for processing these data, there are a limited number of statistical methods for analyzing them, and even fewer that acknowledge the unique nature of individual gene transcription. We introduce a simple and powerful statistical approach, based on a two-stage Poisson model, for modeling RNA sequencing data and testing for biologically important changes in gene expression. The advantages of this approach are demonstrated through simulations and real data applications.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.