\HeaderA{relNetworkB}{Relevance Network analysis}{relNetworkB}
\keyword{methods}{relNetworkB}
\begin{Description}\relax
Function to construct Relevance Networks for one biological type
(Butte's Relevance Network).
\end{Description}
\begin{Usage}
\begin{verbatim}
relNetworkB(data=NULL, gLabelID="GeneName", sLabelID="Classification",
            geneGrp=NULL, path=NULL, samples=NULL,
            type="Rpearson", bRep=1000, ...)
\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 character vector specifying the group to be compared.
\item[\code{type}] type of correlation to be calculated. May be 'Rpearson'
(default), 'pearson', 'kendall', 'spearman' or 'MI'.
\item[\code{bRep}] integer specifying the number of bootstrap permutation to
calculate the significance of correlation values.
\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, \code{\LinkA{robustCorr}{robustCorr}} to calculate
a robust correlation using an idea similar to the leave-one-out or
\code{\LinkA{MI}{MI}} to calculate mutual information values.
\end{Details}
\begin{Value}
The result of this function is an object of class \code{\LinkA{maigesRelNetB}{maigesRelNetB}}.
\end{Value}
\begin{Author}\relax
Gustavo H. Esteves <\email{gesteves@vision.ime.usp.br}>
\end{Author}
\begin{References}\relax
Butte, A.J. and Kohane, I.S. Unsupervised Knowledge discovery in
medical databases using relevance networks. In Proc. AMIA Symp.,
711-715, 1999 (\url{http://www.amia.org/pubs/symposia/D005550.HTM})

Butte, A.J.; Tamayo, P.; Slonim, D.; Golub, T.R. and Kohane,
I.S. Discovering functional relationships between RNA expression and
chemotherapeutic susceptibility using relevance networks, \bold{PNAS},
97, 12182-12186, 2000
(\url{http://www.pnas.org/cgi/content/full/97/22/12182})

Butte, A.J. and Kohane, I.S. Mutual information relevance networks:
functional genomic clustering using pairwise entropy measurements. In
Pacific Symposium on Biocomputing, 5, 415-426, 2000
(\url{http://psb.stanford.edu/psb-online/proceedings/psb00/})
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{cor}{cor}}, \code{\LinkA{robustCorr}{robustCorr}}, \code{\LinkA{MI}{MI}}
\code{\LinkA{maigesRelNetB}{maigesRelNetB}}, \code{\LinkA{plot.maigesRelNetB}{plot.maigesRelNetB}},
\code{\LinkA{image.maigesRelNetB}{image.maigesRelNetB}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## Loading the dataset
data(gastro)

## Constructing the relevance network (Butte's method) for sample
## 'Tissue' equal to 'Neso' for the 1st gene group
gastro.net = relNetworkB(gastro.summ, sLabelID="Tissue", 
  samples="Neso", geneGrp=1, type="Rpearson")

## Constructing the relevance network (Butte's method) for sample
## 'Type' equal to 'Col' for the 1st gene group using the conventional
## pearson correlation
gastro.net = relNetworkB(gastro.summ, sLabelID="Type", 
  samples="Col", geneGrp=1, type="pearson")
\end{ExampleCode}
\end{Examples}

