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Activity Number: 320
Type: Contributed
Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #316062 View Presentation
Title: Bayesian Estimation of Negative Binomial Parameters with Applications to RNA-Seq Data
Author(s): Claudio Fuentes* and Luis Leon-Novelo and Sarah Emerson
Companies: Oregon State University and The University of Texas at Houston and Oregon State University
Keywords: Bayesian Estimation ; Hypothesis Testing ; RNA-seq data ; Negative Binomial ; Overdispersion
Abstract:

RNA-seq data characteristically exhibits large variances, which need to be controlled by marginalization. We first look at the effects of this variability on the MLE of the overdispersion parameter of the negative binomial distribution, and show that the marginal MLE can better control this variation and produce a more reliable estimator. We then formulate a conjugate Bayesian hierarchical model, in which the estimate of overdispersion is a marginalized estimate. Based on this estimator we propose a Bayesian test to detect differentially expressed genes with RNA-seq data. We show that our simple approach is competitive with other negative binomial based procedures.


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