\HeaderA{compCorrGraph}{A function to compute a correlation based graph from Gene
Expression Data}{compCorrGraph}
\keyword{manip}{compCorrGraph}
\begin{Description}\relax
Given a set of gene expression data (an instance of the
\code{ExpressionSet} class) this function computes a graph based on
correlations between the probes.
\end{Description}
\begin{Usage}
\begin{verbatim}
compCorrGraph(eSet, k = 1, tau = 0.6)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{eSet}] An instance of the \code{ExpressionSet} class. 
\item[\code{k}] The power to raise the correlations to. 
\item[\code{tau}] The lower cutoff for absolute correlations. 
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
Zhou et al. describe a method of computing a graph between probes
(genes) based on estimated correlations between probes. This function
implements some of their methods.

Pearson correlations between probes are computed and then these are
raised to the power \code{k}. Any of the resulting estimates that are
less than \code{tau} in absolute value are set to zero.
\end{Details}
\begin{Value}
An instance of the \code{graph} class. With edges and edge weights
determined by applying the algorithm described previously.
\end{Value}
\begin{Author}\relax
R. Gentleman
\end{Author}
\begin{References}\relax
Zhou et al., Transitive functional annotation by
shortest-path analysis of gene expression data.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{compGdist}{compGdist}}
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}

 ## Create an ExpressionSet to work with
 set.seed(123)
 exprMat <- matrix(runif(50 * 5), nrow=50)
 genData <- new("ExpressionSet", exprs=exprMat)

 corrG = compCorrGraph(genData)

\end{ExampleCode}
\end{Examples}

