title: A powerful and flexible approach to the analysis of RNA sequence count data.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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 other alternate methods to handle RNA-Seq data. Although penalized methods have advantages for very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier detection and testing approaches, appears to have favorable characteristics in power and flexibility.
  
  
  BACKGROUND
  An R package containing examples and sample datasets is available at http://www.bios.unc.edu/research/genomic_software/BBSeq
  
  
  BACKGROUND
  yzhou@bios.unc.edu; fwright@bios.unc.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. yzhou@bios.unc.edu
authors.forename: Yi-Hui
authors.initials: YH
authors.lastname: Zhou
authors.forename: Kai
authors.initials: K
authors.lastname: Xia
authors.forename: Fred A
authors.initials: FA
authors.lastname: Wright
mesh_terms: D001483:Base Sequence / Q000235:genetics*; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D008957:Models, Genetic; D015233:Models, Statistical; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D014158:Transcription, Genetic; D059467:Transcriptome / Q000235:genetics*