% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pathCluster.R
\name{predictPathCluster}
\alias{predictPathCluster}
\title{Predicts new paths given a pathCluster model}
\usage{
predictPathCluster(pfit, newdata)
}
\arguments{
\item{pfit}{The pathway cluster model trained by \code{\link{pathCluster}} or \code{\link{pathClassifier}}.}

\item{newdata}{The binary pathway dataset to be assigned a cluster label.}
}
\value{
A list with the following elements:
\tabular{ll}{
\code{labels} \tab a vector indicating the 3M cluster membership. \cr
\code{posterior.probs} \tab a matrix of posterior probabilities for each path belonging to each cluster.
}
}
\description{
Predicts new paths given a pathCluster model.
}
\examples{
	## Prepare a weighted reaction network.
	## Conver a metabolic network to a reaction network.
 data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.
 rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)

	## Assign edge weights based on Affymetrix attributes and microarray dataset.
 # Calculate Pearson's correlation.
	data(ex_microarray)	# Part of ALL dataset.
	rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph,
		weight.method = "cor", use.attr="miriam.uniprot", bootstrap = FALSE)

	## Get ranked paths using probabilistic shortest paths.
 ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
					K=20, minPathSize=8)

	## Convert paths to binary matrix.
	ybinpaths <- pathsToBinary(ranked.p)
	p.cluster <- pathCluster(ybinpaths, M=2)

	## just an example of how to predict cluster membership.
	pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)

}
\seealso{
Other Path clustering & classification methods: \code{\link{pathClassifier}},
  \code{\link{pathCluster}}, \code{\link{pathsToBinary}},
  \code{\link{plotClassifierROC}},
  \code{\link{plotClusterMatrix}},
  \code{\link{plotPathClassifier}},
  \code{\link{plotPathCluster}},
  \code{\link{predictPathClassifier}}
}
\author{
Ichigaku Takigawa

Timothy Hancock
}
\concept{Path clustering & classification methods}