Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
6 Issues per year
IMPACT FACTOR increased in 2014: 1.127
5-year IMPACT FACTOR: 1.537
Rank 47 out of 122 in category Statistics & Probability in the 2014 Thomson Reuters Journal Citation Report/Science Edition
SCImago Journal Rank (SJR): 0.875
Source Normalized Impact per Paper (SNIP): 0.540
Volume 14 (2015)
Volume 13 (2014)
Volume 12 (2013)
Volume 11 (2012)
Volume 10 (2011)
Volume 9 (2010)
Volume 8 (2009)
Volume 6 (2007)
Volume 5 (2006)
Volume 4 (2005)
Volume 3 (2004)
Volume 2 (2003)
Volume 1 (2002)
Most Downloaded Articles
- Sensitivity to prior specification in Bayesian genome-based prediction models by Lehermeier, Christina/ Wimmer, Valentin/ Albrecht, Theresa/ Auinger, Hans-Jürgen/ Gianola, Daniel/ Schmid, Volker J. and Schön, Chris-Carolin
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- A General Framework for Weighted Gene Co-Expression Network Analysis by Zhang, Bin and Horvath, Steve
- Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments by Smyth, Gordon K
- Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates by Lund, Steven P./ Nettleton, Dan/ McCarthy, Davis J. and Smyth, Gordon K.
A Two-Stage Poisson Model for Testing RNA-Seq Data
1Fred Hutchinson Cancer Research Center
Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 10, Issue 1, Pages 1–26, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1627, May 2011
- Published Online:
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.