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) 2014: 0.740
Source Normalized Impact per Paper (SNIP) 2014: 0.470
Impact per Publication (IPP) 2014: 0.926
Mathematical Citation Quotient (MCQ) 2014: 0.17
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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 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
- A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics by Schäfer, Juliane and Strimmer, Korbinian
- 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, ISSN (Print) 2194-6302, 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.
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