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Abstract Details
Activity Number:
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571
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #305732 |
Title:
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Higher-Order Asymptotics for Negative Binomial Regression Inferences from RNA-Sequencing Data
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Author(s):
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Yanming Di*+ and Sarah C Emerson and Daniel W Schafer and Jeff A Kimbrel and Jeff H Chang
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Companies:
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Oregon State University and Oregon State University and Oregon State University and Oregon State University and Oregon State University
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Address:
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Department of Statistics, Corvallis, OR, 97331, United States
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Keywords:
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RNA-Seq ;
gene expression ;
higher-order asymptotics ;
negative binomial ;
regression
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Abstract:
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RNA sequencing (RNA-Seq) has become the technology of choice for quantifying transcriptome and for studying gene expression. The negative binomial (NB) distribution has been shown to be a useful model for frequencies of mapped RNA-Seq reads. An NB regression model is needed to model gene expression as a function of explanatory variables and to compare groups after accounting for other factors. A considerable obstacle in the development of NB regression is the lack of accurate small-sample inference for the NB regression coefficients. We introduce higher-order asymptotic (HOA) techniques for NB regression inferences. We demonstrate that the HOA-adjusted likelihood ratio tests, although derived from large sample size asymptotic theory, are: 1) essentially exact even for the very small sample sizes typical of RNA-Seq studies, 2) superior to other available tests, and 3) therefore overcome the main obstacle to full development of practical negative binomial regression for RNA-Seq analysis. Additionally, this important application to analysis of biological data will draw attention to HOA, a somewhat neglected yet extremely useful development of modern statistical theory.
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