A Powerful and Flexible Approach to the Analysis of RNA Sequence Count Data

• Activity Number: 72
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Yihui Zhou*+ and Fred Andrew Wright
• Companies: The University of North Carolina and The University of North Carolina
• Keywords: statistical genetics ; RNA-seq ; overdispersion ; gene expression

Abstract

A number of penalization and shrinkage approaches have been proposed for the analysis of microarray gene expression data. Similar techniques are now routinely applied to RNA-sequence transcriptional count data, although the value of such shrinkage has not been conclusively established. If penalization is desired, the explicit modeling of mean-variance relationships provides a flexible testing regimen that ``borrows" information across genes, while easily incorporating design effects and additional covariates. We describe BBSeq, which incorporates two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively tested for RNA-Seq data, and (ii) an extension of an expression mean-variance modeling approach to RNA-Seq data, involving modeling of the overdispersion as a function of the mean. Our approaches are flexible, allowing for general handling of discrete experimental factors and continuous covariates. We report comparisons with two recently reported alternative methods to handle RNA-Seq data. The combination of favorable power and flexibility makes BBSeq a worthy addition to the tools used to analyze RNA-Seq data.