@@ -30,4 +30,5 @@ SystemRequirements: libxml2, libSBML (>= 5.5)

 Biarch: TRUE

 biocViews: GraphAndNetwork, Pathways, Network, Clustering,

     Classification

-RoxygenNote: 5.0.1

+RoxygenNote: 6.1.1

+Encoding: UTF-8

@@ -17,6 +17,7 @@ export(getGeneSets)

 export(getPathsAsEIDs)

 export(layoutVertexByAttr)

 export(makeGeneNetwork)

+export(makeMetaboliteNetwork)

 export(makeReactionNetwork)

 export(pathClassifier)

 export(pathCluster)

@@ -35,6 +36,7 @@ export(plotPaths)

 export(predictPathClassifier)

 export(predictPathCluster)

 export(registerMemoryErr)

+export(reindexNetwork)

 export(rmAttribute)

 export(rmSmallCompounds)

 export(samplePaths)

@@ -8,12 +8,12 @@ KGML2igraph(filename, parse.as = c("metabolic", "signaling"),

   expand.complexes = FALSE, verbose = TRUE)

 }

 \arguments{

-\item{filename}{A character vector containing the KGML files to be processed. 

+\item{filename}{A character vector containing the KGML files to be processed.

 If a directory path is provided, all *.xml files in it and its subdirectories are included.}

 \item{parse.as}{Whether to process file into a metabolic or a signaling network.}

-\item{expand.complexes}{Split protein complexes into individual gene nodes. This argument is 

+\item{expand.complexes}{Split protein complexes into individual gene nodes. This argument is

 ignored if \code{parse.as="metabolic"}}

 \item{verbose}{Whether to display the progress of the function.}

@@ -34,8 +34,8 @@ to their corresponding substrates and products. Each reaction vertex has \code{g

 listing all genes associated with the reaction. As a general rule, reactions inherit all annotation

 attributes of its catalyzig genes.

-Signaling network have genes as vertices and edges represent interactions, such as activiation / inhibition. 

-Genes participating in successive reactions are also connected. Signaling parsing method processes <ECrel>, <PPrel> 

+Signaling network have genes as vertices and edges represent interactions, such as activiation / inhibition.

+Genes participating in successive reactions are also connected. Signaling parsing method processes <ECrel>, <PPrel>

 and <PCrel> interactions from KGML files.

 To generate a genome scale network, simply provide a list of files to be parsed, or put all

@@ -43,9 +43,9 @@ file in a directory, as pass the directory path as \code{filename}

 }

 \examples{

 if(is.loaded("readkgmlfile")){ # This is false if libxml2 wasn't available at installation.

-    filename <- system.file("extdata", "hsa00860.xml", package="NetPathMiner") 

+    filename <- system.file("extdata", "hsa00860.xml", package="NetPathMiner")

-    # Process KGML file as a metabolic network 

+    # Process KGML file as a metabolic network

     g <- KGML2igraph(filename)

     plotNetwork(g)

@@ -54,12 +54,12 @@ if(is.loaded("readkgmlfile")){ # This is false if libxml2 wasn't available at in

     plotNetwork(g)

 }

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 Other Database extraction methods: \code{\link{SBML2igraph}},

   \code{\link{biopax2igraph}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Database extraction methods}

@@ -1,19 +1,19 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/netWeight.R

 \name{stdAttrNames}

-\alias{fetchAttribute}

 \alias{stdAttrNames}

+\alias{fetchAttribute}

 \title{MIRIAM annotation attributes}

 \usage{

 stdAttrNames(graph, return.value = c("matches", "graph"))

-fetchAttribute(graph, organism = "Homo sapiens", target.attr, source.attr,

-  bridge.web = NPMdefaults("bridge.web"))

+fetchAttribute(graph, organism = "Homo sapiens", target.attr,

+  source.attr, bridge.web = NPMdefaults("bridge.web"))

 }

 \arguments{

 \item{graph}{An annotated igraph object.}

-\item{return.value}{Specify whether to return the names of matched standard annotations, or modify the 

+\item{return.value}{Specify whether to return the names of matched standard annotations, or modify the

 graph attribute names to match the standards.}

 \item{organism}{The latin name of the organism (Case-sensitive).}

@@ -26,7 +26,7 @@ graph attribute names to match the standards.}

 }

 \value{

 For \code{stdAttrNames}, \code{matches} gives the original attribute names and their MIRIAM version.

-Since this is done by simple text matching, mismatches may occur for ambiguous annotations (such as GO, EC number). 

+Since this is done by simple text matching, mismatches may occur for ambiguous annotations (such as GO, EC number).

 \code{graph} returns the input graph with attribute names standardized.

 For \code{fetchAttribute}, the input \code{graph} with the fetched attribute mapped to vertices.

@@ -35,21 +35,21 @@ For \code{fetchAttribute}, the input \code{graph} with the fetched attribute map

 These functions deals with conforming with MIRIAM annotation guidelines, conversion and mapping between MIRIAM identifiers.

 }

 \examples{

- data(ex_kgml_sig)	# Ras and chemokine signaling pathways in human	

+ data(ex_kgml_sig)	# Ras and chemokine signaling pathways in human

  ## Modify attribute names to match MIRIAM standard annotations.

  graph <- stdAttrNames(ex_kgml_sig, "graph")

- 

+

  # Use Attribute fetcher to get affymetrix probeset IDs for network vertices.

  \dontrun{

-   graph <- fetchAttribute(graph, organism="Homo sapiens", 

+   graph <- fetchAttribute(graph, organism="Homo sapiens",

                    target.attr="miriam.affy.probeset")

  }

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 Other Attribute handling methods: \code{\link{getAttrStatus}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Attribute handling methods}

@@ -18,7 +18,7 @@ This function gets a NetPathMiner default value for a variable.

 \details{

 NetPathMiner defines the following defaults:

 \itemize{

-  \item small.comp.ls Dataframe of ubiquitous metabolites. Used by \code{\link{rmSmallCompounds}}.    

+  \item small.comp.ls Dataframe of ubiquitous metabolites. Used by \code{\link{rmSmallCompounds}}.

   \item bridge Dataframe of attributes supported by Brigde Database. Used by \code{\link{fetchAttribute}}.

   \item bridge.organisms A list of bridge supported organisms. Used by \code{\link{fetchAttribute}}.

   \item bridge.web The base URL for Brigde Database webservices. Used by \code{\link{fetchAttribute}}.

@@ -27,9 +27,8 @@ NetPathMiner defines the following defaults:

 \examples{

  # Get the default list of small compounds (uniquitous metabolites).

  NPMdefaults("small.comp.ls")

- 

+

 }

 \author{

 Ahmed Mohamed

 }

-

@@ -2,19 +2,18 @@

 % Please edit documentation in R/NPM-package.R

 \docType{package}

 \name{NetPathMiner-package}

-\alias{NPM}

-\alias{NetPathMiner}

 \alias{NetPathMiner-package}

+\alias{NetPathMiner}

+\alias{NPM}

 \title{General framework for network extraction, path mining.}

 \description{

 NetPathMiner implements a flexible module-based process flow for network path mining and visualization,

-which can be fully inte-grated with user-customized functions. 

+which can be fully inte-grated with user-customized functions.

 NetPathMiner supports construction of various types of genome scale networks from KGML, SBML and BioPAX

-formats, enabling its utility to most common pathway databases. 

-NetPathMiner also provides different visualization techniques to facilitate the analysis of even 

+formats, enabling its utility to most common pathway databases.

+NetPathMiner also provides different visualization techniques to facilitate the analysis of even

 thousands of output paths.

 }

 \author{

 Ahmed Mohamed \email{mohamed@kuicr.kyoto-u.ac.jp}

 }

-

@@ -8,7 +8,7 @@ SBML2igraph(filename, parse.as = c("metabolic", "signaling"),

   miriam.attr = "all", gene.attr, expand.complexes, verbose = TRUE)

 }

 \arguments{

-\item{filename}{A character vector containing the SBML files to be processed. If a directory path 

+\item{filename}{A character vector containing the SBML files to be processed. If a directory path

 is provided, all *.xml and *.sbml files in it and its subdirectories are included.}

 \item{parse.as}{Whether to process file into a metabolic or a signaling network.}

@@ -17,10 +17,10 @@ is provided, all *.xml and *.sbml files in it and its subdirectories are include

 written in MIRIAM guidelines (see Details) are extracted (Default). If \code{"none"}, then no attributes

 are extracted. Otherwise, only attributes matching those specified are extracted.}

-\item{gene.attr}{An attribute to distinguish \code{species} representing genes from those 

+\item{gene.attr}{An attribute to distinguish \code{species} representing genes from those

 representing small molecules (see Details). Ignored if \code{parse.as="metabolic"}.}

-\item{expand.complexes}{Split protein complexes into individual gene nodes. Ignored if 

+\item{expand.complexes}{Split protein complexes into individual gene nodes. Ignored if

 \code{parse.as="metabolic"}, or when \code{gene.attr} is not provided.}

 \item{verbose}{Whether to display the progress of the function.}

@@ -41,7 +41,7 @@ to their corresponding substrates and products (\code{ListOfSpecies}). Each reac

 listing all \code{modifiers} of this reaction. As a general rule, reactions inherit all annotation

 attributes of its catalyzig genes.

-Signaling network have genes as vertices and edges represent interactions. Since SBML format may 

+Signaling network have genes as vertices and edges represent interactions. Since SBML format may

 represent singling events as \code{reaction}, all species are assumed to be genes (rather than small

 molecules). For a simple path \code{S0 -> R1 -> S1}, in signaling network, the path will be

 \code{S0 -> M(R1) -> S1} where \code{M(R1)} is R1 modifier(s). To ditiguish gene species from small

@@ -55,31 +55,31 @@ by specifying \code{miriam.attr}.

 To generate a genome scale network, simply provide a list of files to be parsed, or put all

 file in a directory, as pass the directory path as \code{filename}

-Note: This function requires libSBML installed (Please see the installation instructions in the Vignette). 

-Some SBML level-3 files may requires additional libraries also (An infomative error will be displayed when 

-parsing such files). Please visit \url{http://sbml.org/Documents/Specifications/SBML_Level_3/Packages} for 

+Note: This function requires libSBML installed (Please see the installation instructions in the Vignette).

+Some SBML level-3 files may requires additional libraries also (An infomative error will be displayed when

+parsing such files). Please visit \url{http://sbml.org/Documents/Specifications/SBML_Level_3/Packages} for

 more information.

 }

 \examples{

 if(is.loaded("readsbmlfile")){ # This is false if libSBML wasn't available at installation.

-    filename <- system.file("extdata", "porphyrin.sbml", package="NetPathMiner") 

+    filename <- system.file("extdata", "porphyrin.sbml", package="NetPathMiner")

-    # Process SBML file as a metabolic network 

+    # Process SBML file as a metabolic network

     g <- SBML2igraph(filename)

     plotNetwork(g)

     # Process SBML file as a signaling network

-    g <- SBML2igraph(filename, parse.as="signaling", 

+    g <- SBML2igraph(filename, parse.as="signaling",

                     gene.attr="miriam.uniprot",expand.complexes=TRUE)

     dev.new()

     plotNetwork(g)

 }

 }

-\author{

-Ahmed Mohamed

-}

 \seealso{

 Other Database extraction methods: \code{\link{KGML2igraph}},

   \code{\link{biopax2igraph}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Database extraction methods}

@@ -13,25 +13,25 @@ assignEdgeWeights(microarray, graph, use.attr, y, weight.method = "cor",

 \item{graph}{An annotated igraph object.}

-\item{use.attr}{An attribute name to map \code{microarray} rows (genes) to graph vertices. The attribute must 

-be annotated in \code{graph}, and the values correspond to \code{rownames} of \code{microarray}. You can check the coverage and 

+\item{use.attr}{An attribute name to map \code{microarray} rows (genes) to graph vertices. The attribute must

+be annotated in \code{graph}, and the values correspond to \code{rownames} of \code{microarray}. You can check the coverage and

 if there are complex vertices using \code{\link{getAttrStatus}}. You can eliminate complexes using \code{\link{expandComplexes}}.}

 \item{y}{Sample labels, given as a factor or a character vector. This must be the same size as the columns of \code{microarray}}

-\item{weight.method}{A function, or a string indicating the name of the function to be used to compute the edge weights. 

-The function is provided with 2 numerical verctors (2 rows from \code{microarray}), and it should return a single numerical 

+\item{weight.method}{A function, or a string indicating the name of the function to be used to compute the edge weights.

+The function is provided with 2 numerical verctors (2 rows from \code{microarray}), and it should return a single numerical

 value (or \code{NA}). The default computes Pearson's correlation.}

 \item{complex.method}{A function, or a string indicating the name of the function to be used in weighting edges connecting complexes.

-If a vertex has >1 attribute value, all possible pairwise weights are first computed, and given to \code{complex.method}. The default 

+If a vertex has >1 attribute value, all possible pairwise weights are first computed, and given to \code{complex.method}. The default

 function is \code{\link[base]{max}}.}

 \item{missing.method}{A function, or a string indicating the name of the function to be used in weighting edges when one of the vertices

 lack expression data. The function is passed all edge weights on the graph. Default is \code{\link[stats]{median}}.}

-\item{same.gene.penalty}{A numerical value to be assigned when 2 adjacent vertices have the same attribute value, since correlation and 

-similarity measure will give perfect scores. Alternatively, \code{same.gene.penalty} can be a function, computing the penalty from all 

+\item{same.gene.penalty}{A numerical value to be assigned when 2 adjacent vertices have the same attribute value, since correlation and

+similarity measure will give perfect scores. Alternatively, \code{same.gene.penalty} can be a function, computing the penalty from all

 edge weights on the graph (excluding same-gene and missing values). The default is to take the \code{\link[stats]{median}}}

 \item{bootstrap}{An integer \code{n}, where the \code{weight.method} is perfomed on \code{n} permutations of the gene profiles, and taking

@@ -40,7 +40,7 @@ the median value. Set it to \code{NA} to disable bootstrapping.}

 \item{verbose}{Print the progress of the function.}

 }

 \value{

-The input graph with \code{edge.weight} as an edge attribute. The attribute can be a list of weights if \code{y} labels 

+The input graph with \code{edge.weight} as an edge attribute. The attribute can be a list of weights if \code{y} labels

 were provided.

 }

 \description{

@@ -55,13 +55,13 @@ This function computes edge weights based on a gene expression profile.

  # 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", 

+		weight.method = "cor", use.attr="miriam.uniprot",

 		y=factor(colnames(ex_microarray)), bootstrap = FALSE)

- 

- # Using Spearman correlation, assigning missing edges to -1 

+

+ # Using Spearman correlation, assigning missing edges to -1

  \dontrun{

-   assignEdgeWeights(microarray, graph, use.attr="miriam.affy.probeset", 

-       y=factor(colnames(microarray)), 

+   assignEdgeWeights(microarray, graph, use.attr="miriam.affy.probeset",

+       y=factor(colnames(microarray)),

        weight.method = function(x1,x2) cor(x1,x2, method="spearman"),

        missing.method = -1)

  }

@@ -70,4 +70,3 @@ This function computes edge weights based on a gene expression profile.

 \author{

 Ahmed Mohamed

 }

-

@@ -12,10 +12,10 @@ biopax2igraph(biopax, parse.as = c("metabolic", "signaling"),

 \item{parse.as}{Whether to process file into a metabolic or a signaling network.}

-\item{expand.complexes}{Split protein complexes into individual gene nodes. Ignored if 

+\item{expand.complexes}{Split protein complexes into individual gene nodes. Ignored if

 \code{parse.as="metabolic"}.}

-\item{inc.sm.molecules}{Include small molecules that are participating in signaling events. Ignored if 

+\item{inc.sm.molecules}{Include small molecules that are participating in signaling events. Ignored if

 \code{parse.as="metabolic"}.}

 \item{verbose}{Whether to display the progress of the function.}

@@ -38,7 +38,7 @@ to their corresponding \code{Left}s and \code{Right}s. Each reaction vertex has

 listing all \code{Catalysis} relationships of this reaction. As a general rule, reactions inherit all annotation

 attributes of its catalyzig genes.

-Signaling network have genes as vertices and edges represent interactions, such as activiation / inhibition. 

+Signaling network have genes as vertices and edges represent interactions, such as activiation / inhibition.

 Genes participating in successive reactions are also connected. Signaling interactions are constructed from

 \code{Control} classes, where edges are drawn from \code{controller} to \code{controlled}.

@@ -48,7 +48,7 @@ MIRIAM guidelines (\code{miraim.db}, where db is the database name).

 \examples{

 if(require(rBiopaxParser)){

     data(ex_biopax)

-    # Process biopax as a metabolic network 

+    # Process biopax as a metabolic network

     g <- biopax2igraph(ex_biopax)

     plotNetwork(g)

@@ -56,11 +56,11 @@ if(require(rBiopaxParser)){

     g <- biopax2igraph(ex_biopax, parse.as="signaling", expand.complexes=TRUE)

 }

 }

-\author{

-Ahmed Mohamed

-}

 \seealso{

 Other Database extraction methods: \code{\link{KGML2igraph}},

   \code{\link{SBML2igraph}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Database extraction methods}

@@ -9,7 +9,7 @@ colorVertexByAttr(graph, attr.name, col.palette = palette())

 \arguments{

 \item{graph}{An annotated igraph object.}

-\item{attr.name}{The attribute name (ex: "pathway") by which vertices will be colored. 

+\item{attr.name}{The attribute name (ex: "pathway") by which vertices will be colored.

 Complex attributes, where a vertex belongs to more than one group, are supported.}

 \item{col.palette}{A color palette, or a palette generating function (ex: \preformatted{col.palette=rainbow}).}

@@ -23,12 +23,9 @@ This function returns a list of colors for vertices, assigned similar colors if

 }

 \examples{

   data("ex_kgml_sig")

-  v.colors <- colorVertexByAttr(ex_kgml_sig, "pathway") 

+  v.colors <- colorVertexByAttr(ex_kgml_sig, "pathway")

   plotNetwork(ex_kgml_sig, vertex.color=v.colors)

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 Other Plotting methods: \code{\link{layoutVertexByAttr}},

@@ -39,4 +36,7 @@ Other Plotting methods: \code{\link{layoutVertexByAttr}},

   \code{\link{plotNetwork}},

   \code{\link{plotPathClassifier}}, \code{\link{plotPaths}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Plotting methods}

@@ -13,4 +13,3 @@ data(ex_biopax)

 ex_biopax

 }

-

@@ -5,7 +5,7 @@

 \alias{ex_kgml_sig}

 \title{Singaling network from KGML example}

 \description{

-An example igraph object representing Ras and chemokine signaling pathways in human 

+An example igraph object representing Ras and chemokine signaling pathways in human

 extracted from KGML files.

 }

 \examples{

@@ -13,4 +13,3 @@ data(ex_kgml_sig)

 plotNetwork(ex_kgml_sig, vertex.color="pathway")

 }

-

@@ -11,4 +11,3 @@ An microarray data example. This is part of the ALL dataset, for demonstration p

 data(ex_microarray)

 }

-

@@ -13,4 +13,3 @@ data(ex_sbml)

 plotNetwork(ex_sbml, vertex.color="compartment.name")

 }

-

@@ -28,11 +28,11 @@ Creates a subnetwork from a ranked path list generated by \code{\link{pathRanker

  # 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", 

+		weight.method = "cor", use.attr="miriam.uniprot",

 		y=factor(colnames(ex_microarray)), bootstrap = FALSE)

 	## Get ranked paths using probabilistic shortest paths.

- ranked.p <- pathRanker(rgraph, method="prob.shortest.path", 

+ ranked.p <- pathRanker(rgraph, method="prob.shortest.path",

 					K=20, minPathSize=6)

 	## Get the subnetwork of paths in reaction graph.

@@ -41,12 +41,12 @@ Creates a subnetwork from a ranked path list generated by \code{\link{pathRanker

 	## Get the subnetwork of paths in the original metabolic graph.

 	metabolic.sub <- getPathsAsEIDs(ranked.p, ex_sbml)

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 Other Path ranking methods: \code{\link{getPathsAsEIDs}},

   \code{\link{pathRanker}}, \code{\link{samplePaths}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Path ranking methods}

@@ -1,11 +1,11 @@

 % Generated by roxygen2: do not edit by hand

 % Please edit documentation in R/netWeight.R

 \name{getAttrStatus}

-\alias{getAttrNames}

 \alias{getAttrStatus}

+\alias{getAttrNames}

 \alias{getAttribute}

-\alias{rmAttribute}

 \alias{setAttribute}

+\alias{rmAttribute}

 \title{Get / Set vertex attribute names and coverage}

 \usage{

 getAttrStatus(graph, pattern = "^miriam.")

@@ -28,7 +28,7 @@ rmAttribute(graph, attr.name)

 \item{attr.value}{A list of attribute values. This must be the same size as the number of vertices.}

 }

 \value{

-For \code{getAttrStatus}, a dataframe summarizing the number of vertices with no (\code{missing}), one (\code{single}) 

+For \code{getAttrStatus}, a dataframe summarizing the number of vertices with no (\code{missing}), one (\code{single})

 or more than one (\code{complex}) attribute value. The coverage% is also reported to each attribute.

 For \code{getAttrNames}, a character vector of attribute names matching the pattern.

@@ -53,7 +53,7 @@ All functions here target NetPathMiner annotations only.

  # Get status of attribute "pathway" only

  getAttrStatus(ex_kgml_sig, "^pathway$")

- 

+

  # Get status of all attributes  starting with "pathway" and "miriam" keywords

  getAttrStatus(ex_kgml_sig, "(^miriam)|(^pathway)")

@@ -65,10 +65,10 @@ All functions here target NetPathMiner annotations only.

  # Remove an attribute from graph

  graph <- rmAttribute(ex_kgml_sig, "miriam.ncbigene")

 }

-\author{

-Ahmed Mohamed

-}

 \seealso{

 Other Attribute handling methods: \code{\link{stdAttrNames}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Attribute handling methods}

@@ -12,7 +12,7 @@ getGeneSetNetworks(graph, use.attr = "pathway", format = c("list",

 \item{use.attr}{The attribute by which vertices are grouped (tepically pathway, or GO)}

-\item{format}{The output format. If "list" is specified, a list of subgraphs are returned (default). 

+\item{format}{The output format. If "list" is specified, a list of subgraphs are returned (default).

 If "pathway-class" is specified, a list of pathway-class objects are returned. Pathway-class

 is used by graphite package to run several methods of topology-based enrichment analyses.}

 }

@@ -30,25 +30,24 @@ common attributes (in the same pathway or compartment).

  # Integration with graphite package

  \dontrun{

  if(require(graphite) & require(clipper) & require(ALL)){

-	genesetnets <- getGeneSetNetworks(ex_kgml_sig, 

+	genesetnets <- getGeneSetNetworks(ex_kgml_sig,

 						use.attr="pathway", format="pathway-class")

-	path <- convertIdentifiers(genesetnets$`Chemokine signaling pathway`, 

+	path <- convertIdentifiers(genesetnets$`Chemokine signaling pathway`,

 						"entrez")

 	genes <- nodes(path)

 	data(ALL)

 	all <- as.matrix(exprs(ALL[1:length(genes),1:20]))

 	classes <- c(rep(1,10), rep(2,10))

 	rownames(all) <- genes

-	

+

 	runClipper(path, all, classes, "mean", pathThr=0.1)

  }

  }

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 \code{\link{getGeneSets}}

 }

-

+\author{

+Ahmed Mohamed

+}

@@ -40,11 +40,10 @@ can be specified through \code{gene.attr} argument.

 	data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.

 	genesets <- getGeneSets(ex_sbml, use.attr="compartment.name", gene.attr="miriam.uniprot")

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 \code{\link{getGeneSetNetworks}}

 }

-

+\author{

+Ahmed Mohamed

+}

@@ -29,25 +29,25 @@ edges on a metabolic network).

  # 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", 

+		weight.method = "cor", use.attr="miriam.uniprot",

 		y=factor(colnames(ex_microarray)), bootstrap = FALSE)

 	## Get ranked paths using probabilistic shortest paths.

- ranked.p <- pathRanker(rgraph, method="prob.shortest.path", 

+ ranked.p <- pathRanker(rgraph, method="prob.shortest.path",

 					K=20, minPathSize=6)

- 

+

 	## Get the edge ids along paths in the reaction graph.

 	path.eids <- getPathsAsEIDs(ranked.p, rgraph)

 	## Get the edge ids along paths in the original metabolic graph.

 	path.eids <- getPathsAsEIDs(ranked.p, ex_sbml)

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 Other Path ranking methods: \code{\link{extractPathNetwork}},

   \code{\link{pathRanker}}, \code{\link{samplePaths}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Path ranking methods}

@@ -12,10 +12,10 @@ layoutVertexByAttr(graph, attr.name, cluster.strength = 1,

 \item{attr.name}{The attribute name by which vertices are laid out.}

-\item{cluster.strength}{A number indicating tie strengths between vertices with the same attribute. 

+\item{cluster.strength}{A number indicating tie strengths between vertices with the same attribute.

 The larger it is, the closer the vertices will be.}

-\item{layout}{A layout function, ideally a force-directed layout fuction, such as 

+\item{layout}{A layout function, ideally a force-directed layout fuction, such as

 \code{\link[igraph]{layout.fruchterman.reingold}} and \code{\link[igraph]{layout.kamada.kawai}}.}

 }

 \value{

@@ -27,15 +27,12 @@ This function generates a layout for igraph objects, keeping vertices with the s

 }

 \examples{

   data("ex_kgml_sig")

-  v.layout <- layoutVertexByAttr(ex_kgml_sig, "pathway") 

+  v.layout <- layoutVertexByAttr(ex_kgml_sig, "pathway")

   plotNetwork(ex_kgml_sig, vertex.color="pathway", layout=v.layout)

   v.layout <- layoutVertexByAttr(ex_kgml_sig, "pathway", cluster.strength=5)

   plotNetwork(ex_kgml_sig, vertex.color="pathway", layout=v.layout)

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 Other Plotting methods: \code{\link{colorVertexByAttr}},

@@ -46,4 +43,7 @@ Other Plotting methods: \code{\link{colorVertexByAttr}},

   \code{\link{plotNetwork}},

   \code{\link{plotPathClassifier}}, \code{\link{plotPaths}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Plotting methods}

@@ -6,8 +6,8 @@

 \title{Expand reactions / complexes into their gene constituents.}

 \usage{

 expandComplexes(graph, v.attr, keep.parent.attr = "^pathway",

-  expansion.method = c("normal", "duplicate"), missing.method = c("keep",

-  "remove", "reconnect"))

+  expansion.method = c("normal", "duplicate"),

+  missing.method = c("keep", "remove", "reconnect"))

 makeGeneNetwork(graph, v.attr = "genes", keep.parent.attr = "^pathway",

   expansion.method = "duplicate", missing.method = "remove")

@@ -20,7 +20,7 @@ makeGeneNetwork(graph, v.attr = "genes", keep.parent.attr = "^pathway",

 \item{keep.parent.attr}{A (List of) \code{\link{regex}} experssions representing attributes to be

 inherited by daughter vertices. If \code{"all"} is passed, all parent attributes are inherited.}

-\item{expansion.method}{If \code{"duplicate"}, attribute values sharing more than one parent vertex 

+\item{expansion.method}{If \code{"duplicate"}, attribute values sharing more than one parent vertex

 are duplicated for each vertex they participate in. For exmaple, if one gene G1 catalyzes reactions

 R1, R2; then G1##R1, and G1##R2 vertices are created. If \code{"normal"} only one vertex (G1) is created,

 and inherit all R1 and R2 connections and attributes.}

@@ -45,33 +45,35 @@ For example, to match a network created from Reactome to a KEGG network, you can

 vertices by "miriam.kegg.compound" attribute.

 }

 \examples{

- ## Make a gene network from a reaction network. 

+ ## Make a gene network from a reaction network.

  data(ex_sbml)	# A bipartite metbaolic network.

  rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)

  ggraph <- makeGeneNetwork(rgraph)

  ## Expand vertices into their contituent genes.

  data(ex_kgml_sig)	# Ras and chemokine signaling pathways in human

-	ggraph <- expandComplexes(ex_kgml_sig, v.attr = "miriam.ncbigene", 

+	ggraph <- expandComplexes(ex_kgml_sig, v.attr = "miriam.ncbigene",

 						keep.parent.attr= c("^pathway", "^compartment"))

  ## Create a separate vertex for each compartment. This is useful in duplicating

 	##  metabolite vertices in a network.

 \dontrun{

- graph <- expandComplexes(graph, v.attr = "compartment", 

+ graph <- expandComplexes(graph, v.attr = "compartment",

         keep.parent.attr = "all",

         expansion.method = "duplicate",

         missing.method = "keep")

 }

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

-Other Network processing methods: \code{\link{makeReactionNetwork}},

+Other Network processing methods: \code{\link{makeMetaboliteNetwork}},

+  \code{\link{makeReactionNetwork}},

+  \code{\link{reindexNetwork}},

   \code{\link{rmSmallCompounds}},

   \code{\link{simplifyReactionNetwork}},

   \code{\link{vertexDeleteReconnect}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Network processing methods}

new file mode 100644

@@ -0,0 +1,36 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/netProcess.R

+\name{makeMetaboliteNetwork}

+\alias{makeMetaboliteNetwork}

+\title{Convert metabolic network to metabolite network.}

+\usage{

+makeMetaboliteNetwork(graph)

+}

+\arguments{

+\item{graph}{A metabolic network.}

+}

+\value{

+A reaction network.

+}

+\description{

+This function removes reaction nodes keeping them as edge attributes. The resulting

+network contains metabolite nodes only, where edges indicate that reaction conversions.

+}

+\examples{

+	## Conver a metabolic network to a metbolite network.

+ data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.

+ mgraph <- makeMetaboliteNetwork(ex_sbml)

+

+}

+\seealso{

+Other Network processing methods: \code{\link{expandComplexes}},

+  \code{\link{makeReactionNetwork}},

+  \code{\link{reindexNetwork}},

+  \code{\link{rmSmallCompounds}},

+  \code{\link{simplifyReactionNetwork}},

+  \code{\link{vertexDeleteReconnect}}

+}

+\author{

+Ahmed Mohamed

+}

+\concept{Network processing methods}

@@ -26,14 +26,16 @@ by one reaction is consumed by the other.

  data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism.

  rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)

-}

-\author{

-Ahmed Mohamed

 }

 \seealso{

 Other Network processing methods: \code{\link{expandComplexes}},

+  \code{\link{makeMetaboliteNetwork}},

+  \code{\link{reindexNetwork}},

   \code{\link{rmSmallCompounds}},

   \code{\link{simplifyReactionNetwork}},

   \code{\link{vertexDeleteReconnect}}

 }

-

+\author{

+Ahmed Mohamed

+}

+\concept{Network processing methods}

@@ -10,7 +10,7 @@ pathClassifier(paths, target.class, M, alpha = 1, lambda = 2,

 \arguments{

 \item{paths}{The training paths computed by \code{\link{pathsToBinary}}}

-\item{target.class}{he label of the targe class to be classified.  This label must be present 

+\item{target.class}{he label of the targe class to be classified.  This label must be present

 as a label within the \code{paths\$y} object}

 \item{M}{Number of components within the paths to be extracted.}

@@ -46,7 +46,7 @@ A list with the following values

 HME3M Markov pathway classifier.

 }

 \details{

-Take care with selection of lambda and alpha - make sure you check that the likelihood 

+Take care with selection of lambda and alpha - make sure you check that the likelihood

 is always increasing.

 }

 \examples{

@@ -59,14 +59,14 @@ is always increasing.

  # 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", 

+		weight.method = "cor", use.attr="miriam.uniprot",

 		y=factor(colnames(ex_microarray)), bootstrap = FALSE)

 	## Get ranked paths using probabilistic shortest paths.

- ranked.p <- pathRanker(rgraph, method="prob.shortest.path", 

+ ranked.p <- pathRanker(rgraph, method="prob.shortest.path",

 					K=20, minPathSize=6)

-	

-	## Convert paths to binary matrix. 

+

+	## Convert paths to binary matrix.

 	ybinpaths <- pathsToBinary(ranked.p)

 	p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)

@@ -77,9 +77,6 @@ is always increasing.

 	plotClassifierROC(p.class)

 	plotClusters(ybinpaths, p.class)

-}

-\author{

-Timothy Hancock and Ichigaku Takigawa

 }

 \references{

 Hancock, Timothy, and Mamitsuka, Hiroshi: A Markov Classification Model for Metabolic Pathways, Workshop on Algorithms in Bioinformatics (WABI) , 2009

@@ -96,4 +93,7 @@ Other Path clustering & classification methods: \code{\link{pathCluster}},

   \code{\link{predictPathClassifier}},

   \code{\link{predictPathCluster}}

 }

-

+\author{

+Timothy Hancock and Ichigaku Takigawa

+}

+\concept{Path clustering & classification methods}

@@ -38,22 +38,17 @@ A list with the following items:

 		weight.method = "cor", use.attr="miriam.uniprot", bootstrap = FALSE)

 	## Get ranked paths using probabilistic shortest paths.

- ranked.p <- pathRanker(rgraph, method="prob.shortest.path", 

+ ranked.p <- pathRanker(rgraph, method="prob.shortest.path",

 					K=20, minPathSize=8)

-	

-	## Convert paths to binary matrix. 

+

+	## Convert paths to binary matrix.

 	ybinpaths <- pathsToBinary(ranked.p)

 	p.cluster <- pathCluster(ybinpaths, M=2)

 	plotClusters(ybinpaths, p.cluster)

- 

-}

-\author{

-Ichigaku Takigawa

-Timothy Hancock

 }

 \references{

-Mamitsuka, H., Okuno, Y., and Yamaguchi, A. 2003. Mining biologically active patterns in 

+Mamitsuka, H., Okuno, Y., and Yamaguchi, A. 2003. Mining biologically active patterns in

 metabolic pathways using microarray expression profiles. SIGKDD Explor. News l. 5, 2 (Dec. 2003), 113-121.

 }

 \seealso{

@@ -66,4 +61,9 @@ Other Path clustering & classification methods: \code{\link{pathClassifier}},

   \code{\link{predictPathClassifier}},

   \code{\link{predictPathCluster}}

 }

+\author{

+Ichigaku Takigawa

+Timothy Hancock

+}

+\concept{Path clustering & classification methods}

@@ -29,25 +29,25 @@ A list of paths where each path has the following items:

 \item{distance}{The sum of the log(ECDF edge weights) along each path.  (a sum of logs is a product)}

 }