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Abstract Details
Activity Number:
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234
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #301469 |
Title:
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A Bayesian Nonparametric Method for Differential Expression Analysis of RNA-Seq Data
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Author(s):
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Yiyi Wang*+ and David B. Dahl
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Companies:
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Texas A & M University and Texas A & M University
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Address:
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Department of Statistics, College Station, TX, 77840,
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Keywords:
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RNA-seq ;
Gene Ontology ;
Differential expression ;
Bayesian nonparametric ;
Next generation sequencing
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Abstract:
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We developed a method for RNA-seq data to identifying differentially expressed genes. Our method builds upon the negative binomial model of others, but uses Gene Ontology (GO) annotations as prior information to assist the clustering process and to improve sensitivity and specificity. Our method allows each gene to have its own parameters and estimates those parameters by a Bayesian nonparametric technique which shares information across genes in the same cluster. The method explicitly calculates the probability that each gene is differentially expressed and the genes are ranked by these probabilities. For any set of genes having high probability of differential expression, the estimated false discovery rate is computed. Thresholds can be adjusted to achieve a desired estimated false discovery rate. We demonstrated with an actual data set.
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