\HeaderA{comp.FC}{Computing Fold Change for Differential Expression}{comp.FC}
\keyword{univar}{comp.FC}
\begin{Description}\relax
\code{comp.FC} returns a function of one argument with bindings for
\code{L}, \code{is.log} and \code{FUN}. This function accepts a
microarray data matrix as its single argment, when evaluated, computes
fold change for each row of the matrix.
\end{Description}
\begin{Usage}
\begin{verbatim}
comp.FC(L = NULL, is.log = TRUE, FUN = mean)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\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{is.log}] A logical variable indicating whether the data has been
logged.
\item[\code{FUN}] The summary statistics function used to calcuate fold
change, the default is set as \code{\LinkA{mean}{mean}}, the user can also
use \code{\LinkA{median}{median}}
\end{ldescription}
.
\end{Arguments}
\begin{Details}\relax
The function returned by \code{comp.FC} calculates fold change for each row
of the matrix, given specific class labels. If \code{is.log=TRUE},
fold change is calculated by substraction; if \code{is.log=FALSE},
fold change is calculated by division.
\end{Details}
\begin{Value}
\code{comp.FC} returns a function with bindings for \code{L},
\code{is.log} and \code{FUN}, which calculates and returns a vector of
fold changes for each row in the data matrix.
\end{Value}
\begin{Author}\relax
Yuanyuan Xiao, \email{yxiao@itsa.ucsf.edu}, \\
Jean Yee Hwa Yang, \email{jean@biostat.ucsf.edu}.
\end{Author}
\begin{SeeAlso}\relax
\code{\LinkA{comp.t}{comp.t}},\code{\LinkA{comp.F}{comp.F}}
\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

f <- comp.FC(L=L)
f.X <- f(X)
\end{ExampleCode}
\end{Examples}

