\HeaderA{deds.pval}{Differential Expression via Distance Summary of p Values from
Multiple Models}{deds.pval}
\keyword{htest}{deds.pval}
\begin{Description}\relax
\code{deds.pval} integrates different \eqn{p}{} values of differential
expression (DE) to rank and select a set of DE genes.
\end{Description}
\begin{Usage}
\begin{verbatim}
deds.pval(X, E = rep(0, ncol(X)), adj = c("fdr", "adjp"), B = 200, nsig = nrow(X))
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{X}] A matrix, with \eqn{m}{} rows corresponding to variables
(hypotheses) and \eqn{n}{} columns corresponding to \eqn{p}{} values from
different statistical models.
\item[\code{E}] A numeric vector indicating the location of the most extreme
\eqn{p}{} values in the direction of differential expression.
\item[\code{adj}] A character string specifying the type of multiple testing
adjustment. \\
If \code{adj="fdr"}, False Discovery Rate is controled and q values
are returned. \\
If \code{adj="adjp"}, ajusted \eqn{p}{} values that controls family wise
type I error rate is returned.
\item[\code{B}] The number of permutations. For a complete enumeration,
\code{B} should be 0 (zero) or any number not less than the total
number of permutations.
\item[\code{nsig}] A numeric variable specifying the number of top genes that
will be returned.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{deds.pval} summarizes \eqn{p}{} values from multiple statistical models
for the evidence of DE. The DEDS methodology treats each gene as
a point corresponding to a gene's vector of DE measures. An "extreme
origin" is defined as the point that indicate DE, typically a vector
of zero \eqn{p}{} values. The  distance from all points to the extreme is
computed and the ranking of a gene for DE is determined by the
closeness of the gene to the extreme. To determine a cutoff for
declaration of DE, null referent distributions are generated using an
approach similar to the gap statistic (see Reference below). DEDS can also summarize
different statistics, see \code{\LinkA{deds.stat}{deds.stat}} and
\code{\LinkA{deds.stat.linkC}{deds.stat.linkC}}.
\end{Details}
\begin{Value}
An object of class \code{\LinkA{DEDS}{DEDS}}. See \code{\LinkA{DEDS-class}{DEDS.Rdash.class}}.
\end{Value}
\begin{Author}\relax
Yuanyuan Xiao, \email{yxiao@itsa.ucsf.edu}, \\
Jean Yee Hwa Yang, \email{jean@biostat.ucsf.edu}.
\end{Author}
\begin{References}\relax
Tibshirani, R., Walther G., and Hastie T. (2000). Estimating the
number of clusters in a dataset via the gap statistic. Department of
Statistics,  Stanford University,
http://www-stat.stanford.edu/~tibs/ftp/gap.ps

Yang, Y.H., Xiao, Y. and Segal M.R.: Selecting differentially expressed
genes from microarray experiment by sets of
statistics. \emph{Bioinformatics} 2005 21:1084-1093.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{deds.stat}{deds.stat}}, \code{\LinkA{deds.stat.linkC}{deds.stat.linkC}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}

\end{ExampleCode}
\end{Examples}

