\HeaderA{xsScores}{Alternative cross-study scores of differential expression}{xsScores}
\keyword{htest}{xsScores}
\keyword{models}{xsScores}
\begin{Description}\relax
Alternative cross-study scores of differential expression
\end{Description}
\begin{Usage}
\begin{verbatim}
xsScores(statistic, N)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{statistic}] a matrix of study-specific estimates of effect size.
Rows are genes and columns are studies.
\item[\code{N}] numerical vector: the number of samples in each study (the
length should be the number of columns in \code{statistic})
\end{ldescription}
\end{Arguments}
\begin{Value}
numeric
\end{Value}
\begin{Author}\relax
R. Scharpf
\end{Author}
\begin{References}\relax
J.K. Choi, U. Yu, S. Kim, and O.J. Yoo (2003), Combining multiple
microarray studies and modeling interstudy variation, Bioinformatics,
19(1) I84-I90.  

E. Garrett-Mayer, G. Parmigiani, X. Zhong, L. Cope, and E. Gabrielson
(2007), Cross-study validation and combined analysis of gene
expression microarray data, Biostatistics, September

R. Scharpf et al., A Bayesian Model for Cross-Study Differential Gene
Expression, Technical Report 158, Johns Hopkins University, Department
of Biostatistics, 2007
\end{References}
\begin{SeeAlso}\relax
the GeneMeta package, \code{\LinkA{ssStatistic}{ssStatistic}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
  data(expressionSetList)
  t <- ssStatistic(statistic="t", phenotypeLabel="adenoVsquamous", esetList=expressionSetList)
  tScores <- xsScores(t, N=nSamples(expressionSetList))
\end{ExampleCode}
\end{Examples}

