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Activity Number: 186
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #312524
Title: Gene-Dependent Normalization of RNA-Seq Data
Author(s): Andrew Lithio*+ and Dan Nettleton
Companies: Iowa State University and Iowa State University
Keywords: RNA-Seq ; Normalization ; Genetics
Abstract:

Next generation sequencing of RNA (RNA-seq) continues to be a powerful tool in genetic research. In particular, RNA-seq allows researchers to identify genes that are differentially expressed between experimental conditions. In order to make comparisons across samples, it is essential to first normalize the data. There are numerous methods for calculating normalization factors in use today. Most methods assume a multiplicative normalization factor that varies across samples but is the same for all genes within each sample. We introduce a new method for normalization that allows the normalization factors to vary across genes within each sample by estimating a gene-specific coefficient for the normalization factors, and then shrinking those estimated coefficients towards a common value. We study the proposed normalization method's effect on the power and false discovery rate of testing for differential expression.


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