Differential Expression in RNA-Seq Data with a New Shrinkage Estimate on Dispersion

• Activity Number: 604
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Chi Wang *+ and Hao Wu and Zhijin Wu
• Companies: University of Kentucky and Emory University and Brown University
• Keywords: second generation sequencing ; RNA-seq ; empirical bayes ; shrinkage estimator ; differential expression

Abstract

The recent developments in RNA-sequencing (RNA-seq) technology have led to a rapid increase in gene expression data in the form of counts. RNA-seq can be used for a variety of applications, however, identifying differential expression (DE) remains a key task in functional genomics. There have been a number of statistical methods for DE detection for RNA-seq data. One common feature of several leading methods is the negative binomial (gamma-Poisson mixture) model. That is, the unobserved gene expression is modeled by a gamma random variable and, given the expression, sequencing counts are modeled as a Poisson random variables. The distinct feature in various methods is how the variance, or dispersion, in the gamma distribution is modeled and estimated. We evaluated several large public RNAseq datasets and find that the estimated dispersion in existing methods does not adequately capture the heterogeneity of biological variance between genes. We present a new empirical Bayes shrinkage estimate of the dispersion parameters and demonstrate improved DE detection.