\HeaderA{ebam}{Empirical Bayes Analysis of Microarrays}{ebam}
\keyword{htest}{ebam}
\begin{Description}\relax
Performs an Empirical Bayes Analysis of Microarrays for a specified value of the
fudge factor \eqn{a_0}{a0}. Modified versions of the t statistics are used.
\end{Description}
\begin{Usage}
\begin{verbatim}
    ebam(a0.out,a0=NA,p0=NA,delta=NA,local.bin=.1,gene.names=NULL,q.values=TRUE,
    R.fold=TRUE,R.unlog=TRUE,na.rm=FALSE,file.out=NA)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{a0.out}] the object to which the output of a previous analysis with \code{find.a0}
was assigned.
\item[\code{a0}] the fudge factor. If \code{NA}, the value suggested by \code{find.a0}
will be used.
\item[\code{p0}] prior probability that a gene is differentially expressed. If not specified
(i.e. \code{NA}), it will automatically be computed.
\item[\code{delta}] a gene will be called differentially expressed, if its posterior
probability of being differentially expressed is larger than or equal to
\code{delta}. By default, the same \code{delta} is used as in \code{find.a0}.
\item[\code{local.bin}] specifies the interval used in the estimation of the local FDR for the
expression score \eqn{z}{}. By default, this interval is \eqn{[z-0.1,z+0.1]}{}.
\item[\code{gene.names}] a vector containing the names of the genes
\item[\code{q.values}] if \code{TRUE} (default), the q-value for each gene will be computed.
\item[\code{R.fold}] if \code{TRUE} (default), the fold change for each differentially
expressed gene will be computed.
\item[\code{R.unlog}] if \code{TRUE}, the anti-log of \code{data} will be used in the computation of
the R.fold. This is recommend if \code{data} contains the log2 transformed gene
expression levels.
\item[\code{na.rm}] if \code{FALSE} (default), the fold change of genes with at least one
missing value will be set to \code{NA}. If \code{TRUE}, missing values will be
replaced by the genewise mean.
\item[\code{file.out}] if specified, general information like the number of significant 
genes and the estimated FDR and gene-specific information like the expression
scores, the q-values, the R fold etc. of the differentially expressed genes
are stored in this file.
\end{ldescription}
\end{Arguments}
\begin{Value}
a plot of the expression scores against their posterior probability of
being differentially expressed, and (optional) a file containing general information
like the estimated FDR and the number of differentially expressed genes and 
gene-specific information about the differentially expressed genes like their names,
their expression scores, q values and their fold changes.

\begin{ldescription}
\item[\code{FDR}] vector containing the estimated \eqn{p_0}{p0}, the number of significant genes,
the number of falsely called genes and the estimated FDR.
\item[\code{ebam.out}] table containing gene-specific information about the differentially
expressed genes.
\item[\code{row.sig.genes}] vector consisting of the row numbers that belong to the differentially
expressed genes.
\item[\code{...}] further objects containing additional information
\end{ldescription}
\end{Value}
\begin{Note}\relax
The number of false positives are computed by \eqn{p_0}{p0} times the number of falsely
called genes.
\end{Note}
\begin{Author}\relax
Holger Schwender, \email{holger.schw@gmx.de}
\end{Author}
\begin{References}\relax
Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001). Empirical Bayes Analysis
of a Microarray Experiment, \emph{JASA}, 96, 1151-1160.

Storey, J.D., and Tibshirani, R. (2003). Statistical significance for genome-wide
experiments, \emph{Technical Report}, Department of Statistics, Stanford University.

Schwender, H. (2003). Assessing the false discovery rate in a statistical analysis of
gene expression data, Chapter 7, \emph{Diploma thesis}, Department of Statistics,
University of Dortmund, \url{http://de.geocities.com/holgerschw/thesis.pdf}.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{find.a0}{find.a0}}   \code{\LinkA{ebam.wilc}{ebam.wilc}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}## Not run: 
    library(multtest)
    # Load the data of Golub et al. (1999). data(golub) contains 
    # a 3051x38 gene expression matrix called golub, a vector of
    # length called golub.cl that consists of the 38 class labels,
    # and a matrix called golub.gnames whose third column contains
    # the gene names.
    data(golub)
    
    # The optimal value for the fudge factor a0 is computed, where
    # possible values of the a0 are 0 and the 0, 0.05 and 0.1 quantile
    # of the standard deviations of the genes. Setting rand=123
    # makes the results reproducible.
    
    find.out<-find.a0(golub,golub.cl,alpha=c(0,0.05,0.1),rand=123)
    
    # Now that we have find the optimal value of a0, an empirical Bayes
    # analysis can be performed.
    
    ebam.out<-ebam(find.out,gene.names=golub.gnames[,3])
    
    # For further analyses the row numbers of the differentially expressed
    # genes are obtained by
    
    ebam.out$row.sig.genes
## End(Not run)\end{ExampleCode}
\end{Examples}