\name{nbinomTest}
\Rdversion{1.1}
\alias{nbinomTest}

\title{
   Test for differences between the base means for two conditions
}
\description{
   This function tests for differences between the base means of two
   conditions (i.e., for differential expression in the case of 
   RNA-Seq). 
}
\usage{
nbinomTest(cds, condA, condB, pvals_only = FALSE)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{cds}{
      a CountDataSet with size factors and raw variance functions
}
  \item{condA}{
      one of the conditions in 'cds'
}
  \item{condB}{
      another one of the conditions in 'cds'
}
  \item{pvals_only}{
      return only a vector of (unadjusted) p values instead of the data frame described below.
}
}
\details{
   See \code{\link{nbinomTestForMatrices}} for more technical informations
}
\value{
A data frame with the following columns:
\item{id}{The ID of the observable, taken from the row names of the counts slots.}
\item{baseMean}{The base mean (i.e., mean of the counts divided by the size factors) for the counts 
for both conditions}
\item{baseMeanA}{The base mean (i.e., mean of the counts divided by the size factors) for the counts 
for condition A}
\item{baseMeanB}{The base mean for condition B}
\item{foldChange}{The ratio meanB/meanA}
\item{log2FoldChange}{The log2 of the fold change}
\item{pval}{The p value for rejecting the null hypothesis 'meanA==meanB'}
\item{padj}{The adjusted p values (adjusted with 'p.adjust( pval, method="BH")')}
\item{resVarA}{The ratio of the row-wise estimate of the base variance of the counts for condition A, divided by
the value predicted with the base variance function from the base mean. If this number is very high, the hit seems
to be a variance outlier and might be false.}
\item{resVarB}{The same for condition B.}
}
\author{
   Simon Anders, sanders@fs.tum.de
}
\examples{
cds <- makeExampleCountDataSet()
cds <- estimateSizeFactors( cds )
cds <- estimateVarianceFunctions( cds )
head( nbinomTest( cds, "A", "B" ) )
}