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Activity Number:
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595
- Recent Methods Development on RNA-Seq Data Analysis
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #330038
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Title:
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Differential Expression Analysis of RNA-Seq Data with Integrated Likelihood Method
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Author(s):
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Yilun Zhang* and David Rocke
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Companies:
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University of Clifornia, Davis and University of California, Davis
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Keywords:
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RNA-seq;
Negative binomial model;
Empirical Bayes;
Integrated Likelihood
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Abstract:
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A popular method to detect changes and differences in gene expression is RNA-Seq, in which the fragments of RNA/DNA are sequenced directly. There are many procedures that have been proposed for assessing the degree of differential expression; some of the most popular are based on the negative binomial distribution. These include edgeR and DESeq2 and some recent work has shown that these methods can produce false positives at many times the expected rate. We present the integrated likelihood method based on negative binomial model which integrates the product of the joint likelihood and a normal prior over support of the dispersion parameter. Our simulations show that our method has higher power performance compared with completing methods on moderate and large sample size and largely avoids the false-positive problem.
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Authors who are presenting talks have a * after their name.
