\HeaderA{stat.norm.exp}{Normalization of log Intensity Ratios across slides / experiments.}{stat.norm.exp}
\keyword{manip}{stat.norm.exp}
\begin{Description}\relax
Performs scale normalization across slides (experiments)
\end{Description}
\begin{Usage}
\begin{verbatim}
stat.norm.exp(X)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{X}] X is a matrix of log intensity ratios \eqn{M=\log_2 (R/G)}{}
The rows of X correspond to genes and columns correspond to different 
hybridizations, that is different slides (experiments).
\end{ldescription}
\end{Arguments}
\begin{Value}
A matrix of normalized log intensity ratios across different slides. 
For the matrix in each of the components, rows correspond to genes
and columns correspond to different hybridizations, that is different 
slides.  This methods scale the matrix such that each column has the 
same median absolute deviation.
\end{Value}
\begin{Author}\relax
Yee Hwa Yang, \email{yeehwa@stat.berkeley.edu} \\
\end{Author}
\begin{References}\relax
Y. H. Yang, S. Dudoit, P. Luu and T. P. Speed. 
Normalization for cDNA Microarray Data. (Statistics, UC Berkeley, 
Tech Report \# 589).
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{ma.func}{ma.func}}, \code{\LinkA{norm.l.func}{norm.l.func}},
\code{\LinkA{norm.pin.func}{norm.pin.func}}, \code{\LinkA{norm.scale.func}{norm.scale.func}}, \code{\LinkA{plot.mva}{plot.mva}}, \code{\LinkA{lowess}{lowess}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
data(MouseArray)
## mouse.setup <- init.grid() 
## mouse.data <- init.data() ## see \emph{init.data} 
mouse.lratio <- stat.ma(mouse.data, mouse.setup)
mouse.norm.lratio <- stat.norm.exp(mouse.lratio$M)
\end{ExampleCode}
\end{Examples}

