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Docs update 1.19

Ahmed Mohamed authored on 01/03/2019 06:31:42
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@@ -34,21 +34,16 @@ Predicts new paths given a pathCluster model.
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 		weight.method = "cor", use.attr="miriam.uniprot", bootstrap = FALSE)
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 	## Get ranked paths using probabilistic shortest paths.
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- ranked.p <- pathRanker(rgraph, method="prob.shortest.path", 
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+ ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
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 					K=20, minPathSize=8)
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-	
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-	## Convert paths to binary matrix. 
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+
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+	## Convert paths to binary matrix.
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 	ybinpaths <- pathsToBinary(ranked.p)
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 	p.cluster <- pathCluster(ybinpaths, M=2)
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 	## just an example of how to predict cluster membership.
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 	pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)
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-	
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-}
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-\author{
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-Ichigaku Takigawa
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-Timothy Hancock
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 }
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 \seealso{
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 Other Path clustering & classification methods: \code{\link{pathClassifier}},
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@@ -59,4 +54,9 @@ Other Path clustering & classification methods: \code{\link{pathClassifier}},
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   \code{\link{plotPathCluster}},
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   \code{\link{predictPathClassifier}}
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 }
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+\author{
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+Ichigaku Takigawa
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+Timothy Hancock
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+}
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+\concept{Path clustering & classification methods}
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update docs using roxygen2

ahmohamed authored on 19/09/2016 02:16:33
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@@ -1,4 +1,5 @@
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-% Generated by roxygen2 (4.0.1): do not edit by hand
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+% Generated by roxygen2: do not edit by hand
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+% Please edit documentation in R/pathCluster.R
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 \name{predictPathCluster}
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 \alias{predictPathCluster}
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 \title{Predicts new paths given a pathCluster model}
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@@ -21,7 +22,7 @@ A list with the following elements:
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 Predicts new paths given a pathCluster model.
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 }
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 \examples{
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-## Prepare a weighted reaction network.
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+	## Prepare a weighted reaction network.
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 	## Conver a metabolic network to a reaction network.
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  data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.
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  rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)
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@@ -33,15 +34,16 @@ Predicts new paths given a pathCluster model.
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 		weight.method = "cor", use.attr="miriam.uniprot", bootstrap = FALSE)
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 	## Get ranked paths using probabilistic shortest paths.
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- ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
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+ ranked.p <- pathRanker(rgraph, method="prob.shortest.path", 
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 					K=20, minPathSize=8)
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-
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-	## Convert paths to binary matrix.
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+	
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+	## Convert paths to binary matrix. 
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 	ybinpaths <- pathsToBinary(ranked.p)
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 	p.cluster <- pathCluster(ybinpaths, M=2)
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 	## just an example of how to predict cluster membership.
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 	pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)
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+	
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 }
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 \author{
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 Ichigaku Takigawa
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@@ -49,14 +51,12 @@ Ichigaku Takigawa
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 Timothy Hancock
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 }
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 \seealso{
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-Other Path clustering & classification methods: \code{\link{pathClassifier}};
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-  \code{\link{pathCluster}}; \code{\link{pathsToBinary}};
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-  \code{\link{plotClassifierROC}};
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+Other Path clustering & classification methods: \code{\link{pathClassifier}},
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+  \code{\link{pathCluster}}, \code{\link{pathsToBinary}},
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+  \code{\link{plotClassifierROC}},
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   \code{\link{plotClusterMatrix}},
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-  \code{\link{plotClusterProbs}},
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-  \code{\link{plotClusters}};
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-  \code{\link{plotPathClassifier}};
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-  \code{\link{plotPathCluster}};
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+  \code{\link{plotPathClassifier}},
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+  \code{\link{plotPathCluster}},
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   \code{\link{predictPathClassifier}}
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 }
62 62
 
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empty commit

Ahmed Mohamed authored on 04/07/2015 00:36:26
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old mode 100644
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new mode 100755
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Updates

added plotCytoscapeGML , Geneset export formats, bug fixes

amfy10 authored on 28/06/2014 15:36:04
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@@ -1,3 +1,4 @@
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+% Generated by roxygen2 (4.0.1): do not edit by hand
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 \name{predictPathCluster}
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 \alias{predictPathCluster}
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 \title{Predicts new paths given a pathCluster model}
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@@ -5,19 +6,16 @@
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 predictPathCluster(pfit, newdata)
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 }
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 \arguments{
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-  \item{pfit}{The pathway cluster model trained by
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-  \code{\link{pathCluster}} or
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-  \code{\link{pathClassifier}}.}
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+\item{pfit}{The pathway cluster model trained by \code{\link{pathCluster}} or \code{\link{pathClassifier}}.}
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-  \item{newdata}{The binary pathway dataset to be assigned
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-  a cluster label.}
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+\item{newdata}{The binary pathway dataset to be assigned a cluster label.}
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 }
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 \value{
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-A list with the following elements: \tabular{ll}{
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-\code{labels} \tab a vector indicating the 3M cluster
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-membership. \cr \code{posterior.probs} \tab a matrix of
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-posterior probabilities for each path belonging to each
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-cluster. }
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+A list with the following elements:
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+\tabular{ll}{
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+\code{labels} \tab a vector indicating the 3M cluster membership. \cr
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+\code{posterior.probs} \tab a matrix of posterior probabilities for each path belonging to each cluster.
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+}
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 }
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 \description{
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 Predicts new paths given a pathCluster model.
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@@ -51,14 +49,14 @@ Ichigaku Takigawa
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 Timothy Hancock
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 }
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 \seealso{
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-Other Path clustering & classification methods:
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-\code{\link{pathClassifier}}; \code{\link{pathCluster}};
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-\code{\link{pathsToBinary}};
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-\code{\link{plotClassifierROC}};
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-\code{\link{plotClusterMatrix}},
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-\code{\link{plotClusterProbs}}, \code{\link{plotClusters}};
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-\code{\link{plotPathClassifier}};
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-\code{\link{plotPathCluster}};
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-\code{\link{predictPathClassifier}}
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+Other Path clustering & classification methods: \code{\link{pathClassifier}};
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+  \code{\link{pathCluster}}; \code{\link{pathsToBinary}};
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+  \code{\link{plotClassifierROC}};
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+  \code{\link{plotClusterMatrix}},
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+  \code{\link{plotClusterProbs}},
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+  \code{\link{plotClusters}};
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+  \code{\link{plotPathClassifier}};
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+  \code{\link{plotPathCluster}};
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+  \code{\link{predictPathClassifier}}
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 }
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0.99.3

Bioconductor, No errors/warnings.

amfy10 authored on 09/04/2014 09:29:08
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@@ -36,11 +36,11 @@ Predicts new paths given a pathCluster model.
36 36
 
37 37
 	## Get ranked paths using probabilistic shortest paths.
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  ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
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-					K=20, minPathSize=6)
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+					K=20, minPathSize=8)
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 	## Convert paths to binary matrix.
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 	ybinpaths <- pathsToBinary(ranked.p)
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-	p.cluster <- pathCluster(ybinpaths, M=3)
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+	p.cluster <- pathCluster(ybinpaths, M=2)
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 	## just an example of how to predict cluster membership.
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 	pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)
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Release 0.99.0 Biocunductor submission

amfy10 authored on 10/03/2014 12:16:44
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new file mode 100644
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@@ -0,0 +1,64 @@
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+\name{predictPathCluster}
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+\alias{predictPathCluster}
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+\title{Predicts new paths given a pathCluster model}
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+\usage{
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+predictPathCluster(pfit, newdata)
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+}
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+\arguments{
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+  \item{pfit}{The pathway cluster model trained by
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+  \code{\link{pathCluster}} or
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+  \code{\link{pathClassifier}}.}
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+
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+  \item{newdata}{The binary pathway dataset to be assigned
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+  a cluster label.}
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+}
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+\value{
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+A list with the following elements: \tabular{ll}{
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+\code{labels} \tab a vector indicating the 3M cluster
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+membership. \cr \code{posterior.probs} \tab a matrix of
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+posterior probabilities for each path belonging to each
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+cluster. }
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+}
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+\description{
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+Predicts new paths given a pathCluster model.
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+}
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+\examples{
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+## Prepare a weighted reaction network.
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+	## Conver a metabolic network to a reaction network.
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+ data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.
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+ rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)
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+
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+	## Assign edge weights based on Affymetrix attributes and microarray dataset.
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+ # Calculate Pearson's correlation.
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+	data(ex_microarray)	# Part of ALL dataset.
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+	rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph,
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+		weight.method = "cor", use.attr="miriam.uniprot", bootstrap = FALSE)
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+
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+	## Get ranked paths using probabilistic shortest paths.
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+ ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
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+					K=20, minPathSize=6)
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+
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+	## Convert paths to binary matrix.
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+	ybinpaths <- pathsToBinary(ranked.p)
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+	p.cluster <- pathCluster(ybinpaths, M=3)
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+
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+	## just an example of how to predict cluster membership.
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+	pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)
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+}
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+\author{
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+Ichigaku Takigawa
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+
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+Timothy Hancock
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+}
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+\seealso{
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+Other Path clustering & classification methods:
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+\code{\link{pathClassifier}}; \code{\link{pathCluster}};
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+\code{\link{pathsToBinary}};
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+\code{\link{plotClassifierROC}};
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+\code{\link{plotClusterMatrix}},
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+\code{\link{plotClusterProbs}}, \code{\link{plotClusters}};
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+\code{\link{plotPathClassifier}};
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+\code{\link{plotPathCluster}};
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+\code{\link{predictPathClassifier}}
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+}
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+