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Statistical Applications in Genetics and Molecular Biology

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

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IMPACT FACTOR 2013: 1.055
Rank 48 out of 119 in category Statistics & Probability in the 2013 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR): 0.875
Source Normalized Impact per Paper (SNIP): 0.540

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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

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