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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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IMPACT FACTOR increased in 2014: 1.127
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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

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A Two-Stage Poisson Model for Testing RNA-Seq Data

Paul L. Auer1 / Rebecca W Doerge2

1Fred Hutchinson Cancer Research Center

2Purdue University

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

Publication History

Published Online:
2011-05-16

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.

Keywords: next-generation sequencing; RNA-Seq; differential expression

Citing Articles

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[1]
Yongchao Dou, Xiaomei Guo, Lingling Yuan, David R. Holding, and Chi Zhang
BioMed Research International, 2015, Volume 2015, Page 1
[3]
Yan Guo, Shilin Zhao, Fei Ye, Quanhu Sheng, and Yu Shyr
BioMed Research International, 2014, Volume 2014, Page 1
[4]
Matthew D. MacManes and Michael B. Eisen
PeerJ, 2013, Volume 1, Page e113

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