title: Moderated statistical tests for assessing differences in tag abundance.
abstract: BACKGROUND
  Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudo-likelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small.
  
  
  RESULTS
  We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts.
  
  
  BACKGROUND
  An R package can be accessed from http://bioinf.wehi.edu.au/resources/
authors: Department of Medical Biology, University of Melbourne, Parkville, Victoria, Australia.
authors: Mark D
authors: MD
authors: Robinson
authors: Gordon K
authors: GK
authors: Smyth
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020224:Expressed Sequence Tags; D020869:Gene Expression Profiling / Q000379:methods*; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D016012:Poisson Distribution; D017422:Sequence Analysis, DNA / Q000379:methods*; D012815:Signal Processing, Computer-Assisted