\HeaderA{relNetworkM}{Relevance Network analysis}{relNetworkM}
\keyword{methods}{relNetworkM}
\begin{Description}\relax
Function to construct Relevance Networks comparing two distinct
biological types.
\end{Description}
\begin{Usage}
\begin{verbatim}
relNetworkM(data=NULL, gLabelID="GeneName", sLabelID="Classification",
            geneGrp=NULL, path=NULL, samples=NULL,
            type="Rpearson", ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{data}] object of class \code{\LinkA{maiges}{maiges}}.
\item[\code{gLabelID}] character string giving the identification of gene label ID.
\item[\code{sLabelID}] character string giving the identification of sample label ID.
\item[\code{geneGrp}] character string (or numeric index) specifying the gene
group to calculate the correlation values between them. If
NULL (together with path) all genes are used.
\item[\code{path}] character string (or numeric index) specifying the gene network
to calculate the correlation values between them. If NULL (together
with geneGrp) all genes are used.
\item[\code{samples}] a named list with two character vectors specifying the
two groups that must be compared.
\item[\code{type}] type of correlation to be calculated. May be 'Rpearson'
(default), 'pearson', 'kendall' or 'spearman'.
\item[\code{...}] additional parameters for functions
\code{\LinkA{robustCorr}{robustCorr}} or \code{\LinkA{cor}{cor}}.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
This method uses the function \code{\LinkA{cor}{cor}} to calculate
the usual correlation values or \code{\LinkA{robustCorr}{robustCorr}} to calculate
a robust correlation using an idea similar to the leave-one-out.
The
correlation values are calculated for pairs of genes in the two groups
specified by the argument \code{samples}, then a Fisher's Z
transformation are done to calculate the significance for the
difference between the two correlation values, this is implemented in
the function \code{\LinkA{compCorr}{compCorr}}. This method was first used in the
work from Gomes et al. (2005).
\end{Details}
\begin{Value}
The result of this function is an object of class \code{\LinkA{maigesRelNetM}{maigesRelNetM}}.
\end{Value}
\begin{Author}\relax
Gustavo H. Esteves <\email{gesteves@vision.ime.usp.br}>
\end{Author}
\begin{References}\relax
Gomes, L.I.; Esteves, G.H.; Carvalho, A.F.; Cristo, E.B.; Hirata Jr.,
R.; Martins, W.K.; Marques, S.M.; Camargo, L.P.; Brentani, H.;
Pelosof, A.; Zitron, C.; Sallum, R.A.; Montagnini, A.; Soares, F.A.;
Neves, E.J. \& Reis, L.F. Expression Profile of Malignant and
Nonmalignant Lesions of Esophagus and Stomach: Differential Activity
of Functional Modules Related to Inflammation and Lipid Metabolism,
\bold{Cancer Research}, 65, 7127-7136, 2005
(\url{http://cancerres.aacrjournals.org/cgi/content/abstract/65/16/7127})
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{cor}{cor}}, \code{\LinkA{robustCorr}{robustCorr}}
\code{\LinkA{compCorr}{compCorr}}, \code{\LinkA{maigesRelNetM}{maigesRelNetM}},
\code{\LinkA{plot.maigesRelNetM}{plot.maigesRelNetM}},
\code{\LinkA{image.maigesRelNetM}{image.maigesRelNetM}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## Loading the dataset
data(gastro)

## Constructing the relevance network for sample
## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group
gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", 
  samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11,
  type="Rpearson")
\end{ExampleCode}
\end{Examples}

