\HeaderA{deds.stat.linkC}{Differentail Expression via Distance Summary of Multiple Statistics}{deds.stat.linkC}
\keyword{htest}{deds.stat.linkC}
\begin{Description}\relax
\code{deds.stat.linkC} integrates different statistics of differential
expression (DE) to rank and select a set of DE genes.
\end{Description}
\begin{Usage}
\begin{verbatim}
deds.stat.linkC(X, L, B = 1000, tests = c("t", "fc", "sam"), tail =
c("abs", "lower", "higher"), extras = NULL, distance = c("weuclid",
"euclid"), adj = c("fdr", "adjp"), nsig = nrow(X), quick = TRUE)
\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 observations.
In the case of gene expression data, rows correspond to genes and
columns to mRNA samples. The data can be read using \code{\LinkA{read.table}{read.table}}.
\item[\code{L}] A vector of integers corresponding to observation (column)
class labels. For \eqn{k}{} classes, the labels must be integers
between 0 and \eqn{k-1}{}.
\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{tests}] A character vector specifying the statistics to be
used to test the null hypothesis of no association between the
variables and the class labels, \code{test} could be any of the
following:  \\
\Tabular{ll}{
\code{"t"}: & one or two sample t-statistics; \\
\code{"f"}: & F-statistics;\\
\code{"fc"}: & fold changes among classes;\\
\code{"sam"}:& SAM-statistics; \\
\code{"modt"}: & moderated t-statistics; \\
\code{"modt"}: & moderated F-statistics; \\
\code{"B"}: & B-statistics.}

\item[\code{tail}] A character string specifying the type of rejection
region.\\
If \code{side="abs"}, two-tailed tests, the null hypothesis is
rejected for large absolute values of the test statistic.\\
If \code{side="higher"}, one-tailed tests, the null hypothesis
is rejected for large values of the test statistic.\\
If \code{side="lower"}, one-tailed tests,  the null hypothesis is
rejected for small values of the test statistic.

\item[\code{extras}] Extra parameter needed for the test specified; see
\code{\LinkA{deds.genExtra}{deds.genExtra}}.
\item[\code{distance}] A character string specifying the type of distance
measure used for the calculation of the distance to the extreme
point (E). \\
If \code{distance="weuclid"}, weighted euclidean distance, the
weight for statistic \eqn{t}{} is \eqn{\frac{1}{MAD(t)}}{1/MAD(t)}; \\
If \code{distance="euclid"}, euclidean distance.

\item[\code{adj}] A character string specifying the type of multiple testing
adjustment. \\
If \code{adj="fdr"}, False Discovery Rate is controled and \eqn{q}{}
values are returned. \\
If \code{adj="adjp"}, ajusted \eqn{p}{} values that controls family wise
type I error rate are returned.
\item[\code{nsig}] If \code{adj = "fdr"}, \code{nsig} specifies the number of top
differentially expressed genes whose \eqn{q}{} values will be calculated; we recommend 
setting \code{nsig < m}, as the computation of \eqn{q}{} values will be extensive. \eqn{q}{} values
for the rest of genes will be approximated to 1. If \code{adj = "adjp"}, the 
calculation of the adjusted \eqn{p}{} values will be for the whole dataset.
\item[\code{quick}] A logical variable specifying if a quick but memory
requiring procedure will be selected. If \code{quick=TRUE},
permutation will be carried out once and stored in memory; If
\code{quick=FALSE} a fixed seeded sampling procedure will be
employed, which requires more computation time as the permutation
will be carried out twice, but will not use extra memory for storage.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{deds.stat.linkC} summarizes multiple statistical measures 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 maxima of all statistics and the
distance from all points to the extreme is computed and 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 by permuting the data matrix.

Statistical measures currently in the DEDS package include t statistics
(\code{tests="t"}), fold changes (\code{tests="fc"}), F
statistics (\code{tests="f"}), SAM (\code{tests="sam"}), moderated
t (\code{tests="modt"}), moderated F statistics
(\code{tests="modf"}), and B statistics (\code{tests="B"}). The
function \code{deds.stat.linkC} interfaces to C functions for the
tests and the computation of DEDS. For more flexibility, the user can
also use \code{deds.stat} which has the same functionality as
\code{deds.stat.linkC} but is written completely in R (therefore
slower) and the user can supply their own function for a statistic
not covered in the DEDS package.

DEDS can also summarize p values from different statistical models, see
\code{\LinkA{deds.pval}{deds.pval}}.
\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
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.pval}{deds.pval}}, \code{\LinkA{deds.stat}{deds.stat}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
X <- matrix(rnorm(1000,0,0.5), nc=10)
L <- rep(0:1,c(5,5))

# genes 1-10 are differentially expressed
X[1:10,6:10]<-X[1:10,6:10]+1
# DEDS summarizing t, fc and sam
d <- deds.stat.linkC(X, L, B=200)
\end{ExampleCode}
\end{Examples}

