@@ -1,27 +1,32 @@

 Package: NetPathMiner

-Version: 1.1.3

+Version: 1.1.6

 Date: 2014 onwards

 Title: NetPathMiner for Biological Network Construction, Path Mining

-        and Visualization

+    and Visualization

 Author: Ahmed Mohamed <mohamed@kuicr.kyoto-u.ac.jp>, Tim Hancock

-        <timothy.hancock@kuicr.kyoto-u.ac.jp>, Ichigaku Takigawa

-        <takigawa@kuicr.kyoto-u.ac.jp>, Nicolas Wicker

-        <nicolas.wicker@unistra.fr>

+    <timothy.hancock@kuicr.kyoto-u.ac.jp>, Ichigaku Takigawa

+    <takigawa@kuicr.kyoto-u.ac.jp>, Nicolas Wicker

+    <nicolas.wicker@unistra.fr>

 Maintainer: Ahmed Mohamed <mohamed@kuicr.kyoto-u.ac.jp>

 Description: NetPathMiner is a general framework for network path

-        mining using genome-scale networks. It constructs networks from

-        KGML, SBML and BioPAX files, providing three network

-        representations, metabolic, reaction and gene representations.

-        NetPathMiner finds active paths and applies machine learning

-        methods to summarize found paths for easy interpretation. It

-        also provides static and interactive visualizations of networks

-        and paths to aid manual investigation.

-Depends: R (>= 3.0.2), igraph (>= 0.6)

-Suggests: rBiopaxParser (>= 2.1), RCurl, RCytoscape

+    mining using genome-scale networks. It constructs networks from

+    KGML, SBML and BioPAX files, providing three network

+    representations, metabolic, reaction and gene representations.

+    NetPathMiner finds active paths and applies machine learning

+    methods to summarize found paths for easy interpretation. It

+    also provides static and interactive visualizations of networks

+    and paths to aid manual investigation.

+Depends:

+    R (>= 3.0.2),

+    igraph (>= 0.6)

+Suggests:

+    rBiopaxParser (>= 2.1),

+    RCurl,

+    RCytoscape

 License: GPL (>= 2)

 URL: https://github.com/ahmohamed/NetPathMiner

 NeedsCompilation: yes

 SystemRequirements: libxml2, libSBML (>= 5.5)

 Biarch: TRUE

 biocViews: GraphAndNetwork, Pathways, Network, Clustering,

-        Classification

+    Classification

@@ -1,3 +1,5 @@

+# Generated by roxygen2 (4.0.1): do not edit by hand

+

 export(KGML2igraph)

 export(NPMdefaults)

 export(SBML2igraph)

@@ -26,6 +28,7 @@ export(plotClusterMatrix)

 export(plotClusterProbs)

 export(plotClusters)

 export(plotCytoscape)

+export(plotCytoscapeGML)

 export(plotNetwork)

 export(plotPathClassifier)

 export(plotPathCluster)

@@ -0,0 +1,12 @@

+NetPathMiner 1.1.6

+===========

+

+* plotCytoscapeGML: a new function to export network plots as GML files, for Cytoscape 3.0 compatibility.

+* getGetsetNetworks now can return pathway-class objects, used by graphite package, which allows fast topology-based geneset analyses.

+* getGenesets now can export the results in GMT file format, readily parsed by most GSEA packages. 

+* Bug fixes on KGML signaling network construction.

+

+NetPathMiner 1.1.3

+===========

+

+* Bioconductor stable release on the development branch

@@ -144,7 +144,7 @@ KGML2igraph <- function(filename, parse.as=c("metabolic","signaling"), expand.co

 KGML_signal <- function(fileList, expand.complexes, verbose){

-    if(verbose) message("Parsing KGML files as metabolic networks")

+    if(verbose) message("Parsing KGML files as signaling networks")

     zkgml <- .Call("readkgml_sign", FILENAME = fileList, 

                 EXPAND_COMPLEXES = expand.complexes, VERBOSE=verbose)

@@ -377,6 +377,8 @@ expandComplexes <- function(graph, v.attr,

     for(i in list.edge.attributes(graph)){

         gout <- set.edge.attribute(gout, i, value=get.edge.attribute(graph, i)[z$e.parents] )

     }

+    

+    gout$source <- graph$source

     return(gout);

 }

@@ -493,9 +493,11 @@ assignEdgeWeights <- function(microarray, graph, use.attr, y, weight.method="cor

 #' 

 #' @param graph An annotated igraph object..

 #' @param use.attr The attribute by which vertices are grouped (tepically pathway, or GO)

-#' @param gene.attr The attribute listing genes annotated with each vertex (ex: miriam.ncbigene, miriam.uniprot, ...) 

+#' @param gene.attr The attribute listing genes annotated with each vertex (ex: miriam.ncbigene, miriam.uniprot, ...)

+#' @param gmt.file Optinal. If provided, Results are exported to a GMT file. GMT files are readily used

+#' by most gene set analysis packages. 

 #' 

-#' @return A list of genesets.

+#' @return A list of genesets or written to gmt file if provided.

 #' 

 #' @author Ahmed Mohamed

 #' @seealso \code{\link{getGeneSetNetworks}}

@@ -503,8 +505,19 @@ assignEdgeWeights <- function(microarray, graph, use.attr, y, weight.method="cor

 #' @examples

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

 #'  genesets <- getGeneSets(ex_kgml_sig, use.attr="pathway", gene.attr="miriam.ncbigene")

+#' 

+#' \donttest{	

+#' 	# Write the genesets in a GMT file, and read it using GSEABase package.

+#'  getGeneSets(ex_kgml_sig, use.attr="pathway", gene.attr="miriam.ncbigene", gmt.file="kgml.gmt")

+#' 	if(require(GSEABase))

+#' 		toGmt("kgml.gmt")

+#' }

+#' 	

+#' 	# Create genesets using compartment information

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

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

 #'

-getGeneSets <- function(graph, use.attr="pathway", gene.attr="genes"){    

+getGeneSets <- function(graph, use.attr="pathway", gene.attr="genes", gmt.file){    

     attr.names <- getAttrNames(graph)

     if(!use.attr %in% getAttrNames(graph))

         stop(use.attr, ": attribute not found in graph.")

@@ -521,7 +534,23 @@ getGeneSets <- function(graph, use.attr="pathway", gene.attr="genes"){

     sets <- split(as.numeric(attr[,1]), attr[,2])

     genesets <- lapply(sets, function(x) unlist(genes[x]))

-    return(genesets)

+    if(!missing(gmt.file)){

+        pos <- regexpr("\\.([[:alnum:]]+)$", gmt.file)

+        ext <- ifelse(pos > -1L, substring(gmt.file, pos + 1L), "")

+        ext <- tolower(ext)

+        

+        if(!ext == "gmt")

+            stop("File format not suuported. Please rename the file as *.gmt")

+        

+        gmt <- paste(names(genesets), paste(graph$source, use.attr), 

+                    lapply(genesets, paste, sep="", collapse="\t"), 

+                    sep="\t", collapse="\n")

+        

+        write(gmt, file=gmt.file)

+        

+    }else{

+        return(genesets)

+    }

 }

 #' Generate geneset networks from an annotated network.

@@ -531,8 +560,11 @@ getGeneSets <- function(graph, use.attr="pathway", gene.attr="genes"){

 #' 

 #' @param graph An annotated igraph object..

 #' @param use.attr The attribute by which vertices are grouped (tepically pathway, or GO)

+#' @param 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. \link[graphite]{pathway-class} 

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

 #' 

-#' @return A list of geneset networks.

+#' @return A list of geneset networks as igraph or \link[graphite]{pathway-class} objects.

 #' 

 #' @author Ahmed Mohamed

 #' @seealso \code{\link{getGeneSets}}

@@ -540,8 +572,25 @@ getGeneSets <- function(graph, use.attr="pathway", gene.attr="genes"){

 #' @examples

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

 #'  genesetnets <- getGeneSetNetworks(ex_kgml_sig, use.attr="pathway")

+#' 

+#'  # Integration with graphite package

+#' \donttest{

+#'  if(require(graphite) & require(clipper) & require(ALL)){

+#'		genesetnets <- getGeneSetNetworks(ex_kgml_sig, 

+#' 						use.attr="pathway", format="pathway-class")

+#'		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)

+#'  }

+#' } 

 #'

-getGeneSetNetworks <- function(graph, use.attr="pathway"){    

+getGeneSetNetworks <- function(graph, use.attr="pathway", format=c("list", "pathway-class")){    

     attr.names <- getAttrNames(graph)

     if(!use.attr %in% getAttrNames(graph))

         stop(use.attr, ": attribute not found in graph.")

@@ -554,6 +603,68 @@ getGeneSetNetworks <- function(graph, use.attr="pathway"){

     sets <- split(as.numeric(attr[,1]), attr[,2])

     genesetnet <- lapply(sets, function(x) induced.subgraph(graph, x))

+    if(!missing(format) && format=="pathway-class"){

+        pathway <- setClass("pathway",

+                representation(title="vector",

+                        nodes="vector",

+                        edges="data.frame",

+                        ident="vector",

+                        database="vector",

+                        timestamp="Date"))

+        

+        stds <- stdAttrNames(graph, "matches")

+        if("miriam.ncbigene" %in% stds$standard){

+            annotation <- "miriam.ncbigene" 

+        }else if("miriam.uniprot" %in% stds$standard){

+            annotation <- "miriam.uniprot"

+        }else{

+            stop("Provided graph doesn't contain Entrez IDs nor UniProt IDs needed for graphite package")

+        }

+        

+        genesetnet <- lapply(genesetnet, function(g)

+                            try(

+                                expandComplexes(g, rownames(stds[stds$standard==annotation,]), missing.method="remove"),

+                            silent=TRUE)

+                )

+        

+        genesetnet <- genesetnet[ !sapply(genesetnet, class) == "try-error" ]

+                

+        genesetnet <- genesetnet[ !sapply(genesetnet, ecount) == 0 ]

+        ann.prefix <- ifelse(annotation == "miriam.ncbigene", "EntrezGene", "UniProt") 

+        genesetnet <- lapply(genesetnet, function(g) 

+                                        set.vertex.attribute(g, "name", 

+                                                value=paste(ann.prefix, V(g)$name, sep=":") ) 

+                            )

+                            

+        prepareEdges <- function(g){

+            ret <- data.frame( get.edgelist(g), 

+                    direction="directed", 

+                    type=as.character(E(g)$attr)

+            )

+            names(ret)[1:2] <- c("src", "dest")

+            ret$src <- as.character(ret$src)

+            ret$dest <- as.character(ret$dest)

+            return(ret)

+        }

+                

+                edges <- lapply(genesetnet, function(g){

+                                                    

+                } )

+        

+        pathwayset <- mapply(function(name, g){

+                    pathway(title=name, 

+                            nodes=V(g)$name,

+                            edges=prepareEdges(g),

+                            ident="native", 

+                            database=paste("NetPathMiner(",graph$source,")", sep=""),

+                            timestamp=Sys.Date()

+                    )                    

+                }, names(genesetnet), genesetnet

+            )

+        

+        return(pathwayset)

+    }# End pathway-class return

+    

     return(genesetnet)

 }

@@ -493,7 +493,7 @@ processNetwork <- function(graph, start, end, scale=c("ecdf", "rescale"), normal

     # Get edge.weights and apply ecdf on each column

     edge.weights <- do.call("rbind", as.list(E(graph)$edge.weights))

-    cat("weights: ", nrow(edge.weights))   

+    

     if(sum(!is.finite(edge.weights))>0){

         warning("Edge weights contain non-finite numbers. Setting them to the minimum edge weight")

@@ -512,7 +512,7 @@ processNetwork <- function(graph, start, end, scale=c("ecdf", "rescale"), normal

     if (ncol(edge.probs) > 1 & normalize == TRUE) {

         edge.probs <- edge.probs / rowSums(edge.probs)

     }

-    cat("weights: ", nrow(edge.probs))

+

     if(scale=="ecdf")

         edge.probs <- -log(edge.probs)

@@ -521,7 +521,7 @@ processNetwork <- function(graph, start, end, scale=c("ecdf", "rescale"), normal

     edgelist = get.edgelist(graph, names=FALSE)

     edgelist = data.frame(from=edgelist[,1], to=edgelist[,2], label=unlist(label), stringsAsFactors=FALSE)

-    cat("edges: ", nrow(edgelist), ",weights: ", nrow(edge.probs))

+

     return(list(nodes=V(graph)$name, edges=edgelist, weights=edge.probs))

 }

@@ -295,12 +295,16 @@ colorVertexByAttr <- function(graph, attr.name, col.palette = palette()){

 #' Plots an annotated igraph object in Cytoscape.

 #' 

-#' This function uses RCytoscape interface to plot igraph object in Cytoscape, enabling

-#' interactive investigation of the network. The function requires an open Cytoscape window,

-#' with CytoscapeRPC plugin activated.

+#' Thess functions provide ways to plot igraph object in Cytoscape, enabling

+#' interactive investigation of the network. \link{plotCytoscape} uses RCytoscape 

+#' interface to plot graphs in Cytoscape directly form R. The function is compatible with Cytoscape

+#' 2.8.3 or lower, and requires an open Cytoscape window, with CytoscapeRPC plugin installed and activated.

+#' \link{plotCytoscapeGML} exports the network plot in GML format, that can be later imported into Cytoscape

+#' (using "import network from file" option). This fuction is compatible with all Cytoscape versions. 

 #' 

 #' @param graph An annotated igraph object.

 #' @param title Will be set as a window title in Cytoscape.

+#' @param file Output GML file name to which the network plot is exported.

 #' @param layout Either a graph layout function, or a two-column matrix specifiying vertex coordinates.

 #' @param vertex.size Vertex size. If missing, the vertex attribute "size" (\preformatted{V(g)$size)}) will be used.

 #' @param vertex.label Vertex labels. If missing, the vertex attribute "label" (\preformatted{V(g)$label)}) will be used.

@@ -317,13 +321,16 @@ colorVertexByAttr <- function(graph, attr.name, col.palette = palette()){

 #' 

 #' @author Ahmed Mohamed

 #' @family Plotting methods

+#' @rdname plotCytoscape

 #' @export

 #' @examples

+#'  data("ex_sbml")

+#' 	rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE)

+#'  v.layout <- layoutVertexByAttr(rgraph, "compartment") 

+#' 	v.color <- colorVertexByAttr(rgraph, "compartment")

+#' 

 #' \dontrun{

-#'  data("ex_kgml_sig")

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

-#' 	

-#' 	cw<-plotCytoscape(ex_kgml_sig, title="example", layout = v.layout,

+#' 	cw<-plotCytoscape(rgraph, title="example", layout = v.layout,

 #' 				vertex.size = 5, vertex.color = v.color)

 #' } 

 #' 

@@ -343,14 +350,14 @@ plotCytoscape <- function(graph, title, layout=layout.auto,

     for(i in 1:length(v.attrs)){

         nel <- RCytoscape::initNodeAttribute(nel, names(v.attrs)[[i]],

                                 ifelse(v.attrs[[i]], "numeric", "char"),

-                                ifelse(v.attrs, 1, ""))

+                                ifelse(v.attrs[[i]], 1, ""))

     }

     e.attrs <- sapply(list.edge.attributes(graph), function(x) is.numeric(get.edge.attribute(graph,x)))

-    for(i in 1:length(e.attrs)){

-        nel <- RCytoscape::initEdgeAttribute(nel, names(e.attrs)[[i]],

+    for(i in names(e.attrs)){

+        nel <- RCytoscape::initEdgeAttribute(nel, i,

                 ifelse(e.attrs[[i]], "numeric", "char"),

-                ifelse(e.attrs, 1, ""))

+                ifelse(e.attrs[[i]], 1, ""))

     }

     cw <- RCytoscape::new.CytoscapeWindow (title, graph=nel)

@@ -446,6 +453,137 @@ plotCytoscape <- function(graph, title, layout=layout.auto,

 }

+#' @return For \code{plotCytoscapeGML}, results are written to file.

+#' 

+#' @export

+#' @rdname plotCytoscape

+#' @examples

+#'  # Export network plot to GML file

+#'  plotCytoscapeGML(rgraph, file="example.gml", layout=v.layout, 

+#' 				vertex.color=v.color, vertex.size=10)

+#' 

+plotCytoscapeGML <- function(graph, file, layout=layout.auto, 

+        vertex.size, vertex.label, vertex.shape, vertex.color, edge.color){

+    

+    #v.size

+    if(!missing(vertex.size)){

+        if(length(vertex.size)==1)

+            vertex.size <- rep(vertex.size, vcount(graph))

+        

+        vertex.size <- as.integer(vertex.size)

+        if(length(vertex.size) != vcount(graph)){

+            warning("Vertex sizes length and number of vertices don't match")

+        }else{

+            V(graph)$size <- vertex.size

+        }

+    }else if(is.null(V(graph)$size)){

+        V(graph)$size <- 15

+    }

+    

+    #v.label

+    if(!missing(vertex.label)){

+        if(length(vertex.label) == vcount(graph)){

+            V(graph)$label <- as.character(vertex.label)

+        }else

+            warning("Vertex lebels length and number of vertices don't match")        

+    }

+    

+    #v.shape

+    igraphShape2Cyto <- function(x){

+        return(ifelse( x %in% c("square","rectangle","vrectangle"), "rect",

+                        ifelse(x %in% c("csquare","crectangle"), "round_rect", "ellipse" ))

+        )

+    }

+    if(!missing(vertex.shape)){

+        vertex.shape <- as.character(vertex.shape)

+        if(length(vertex.shape)==1)

+            vertex.shape <- rep(vertex.shape, vcount(graph))

+        

+        if(length(vertex.shape) == vcount(graph)){

+            V(graph)$type <- vertex.shape

+        }else

+            warning("Vertex shapes length and number of vertices don't match")

+        

+    }else if(!is.null(V(graph)$shape)){

+        V(graph)$type <- igraphShape2Cyto(V(graph)$shape)

+    }else{

+        V(graph)$type <- "ellipse"

+    }

+    

+    #v.color

+    col2hex <- function(x) return( rgb(t(col2rgb( x )/255)) )

+    if(!missing(vertex.color)){

+        vertex.color <- as.character(vertex.color)

+        if(length(vertex.color)==1)

+            vertex.color <- rep(vertex.color, vcount(graph))        

+        

+        if(length(vertex.color) == vcount(graph)){

+            V(graph)$fill <- col2hex(vertex.color)

+        }else{

+            warning("Vertex colors length and number of vertices don't match.\n

+                            Multi-colored vertices are not supported in Cytoscape plots")

+        }

+    }else if(!is.null(V(graph)$color)){

+        V(graph)$fill <- col2hex(V(graph)$color)

+    }else{

+        V(graph)$fill <- col2hex("skyblue")		

+    }

+    

+    #e.color

+    if(!missing(edge.color)){

+        edge.color <- col2hex(edge.color)

+        if(length(edge.color)==1)

+            edge.color <- rep(edge.color, ecount(graph))

+        

+        if(length(edge.color) == ecount(graph)){

+            E(graph)$fill <- col2hex(edge.color)

+        }else

+            warning("Edge colors length and number of vertices don't match")

+        

+    }else if(!is.null(E(graph)$color)){

+        E(graph)$fill <- col2hex(E(graph)$color)

+    }else{

+        E(graph)$fill <- col2hex("grey")

+    }

+    

+    if(!is.null(layout)){

+        if(is.function(layout)){

+            l <- layout(graph) #* max(V(graph)$size)

+            V(graph)$x <- l[,1]

+            V(graph)$y <- l[,2]

+        }else if(ncol(layout)==2 && nrow(layout)==vcount(graph)){

+            V(graph)$x <- layout[,1]

+            V(graph)$y <- layout[,2]

+        }else{

+            warning("Incompatible layout dimensions. It should be 2 columns and rows matching number of vertices")

+        }		

+    }

+    

+    attr <- get.data.frame(graph, what="both")

+    node.attr <- attr$vertices[!sapply(attr$vertices, is.list)]

+    node.graphics <- node.attr[ ,colnames(node.attr) %in% c("x", "y", "size", "type", "fill"), drop=FALSE]

+    

+    node.attr <- node.attr[ ,!colnames(node.attr) %in% c("x", "y", "size", "type", "fill", "shape", "color"), drop=FALSE]

+    node.attr <- cbind(id=as.numeric(V(graph)), node.attr, graphics=toGML(node.graphics, "graphics"))

+    nodes.gml <- toGML(node.attr, "node", "\n")

+    

+    

+    edge.attr <- attr$edges[!sapply(attr$edges, is.list)]

+    names(edge.attr)[1:2] <- c("source", "target")

+    edge.attr[1:2] <- get.edgelist(graph,names=FALSE)

+    edge.graphics <- edge.attr[ ,colnames(edge.attr) %in% c("size", "type", "fill"), drop=FALSE]

+    

+    edge.attr <- edge.attr[ ,!colnames(edge.attr) %in% c("size", "type", "fill", "shape", "color"), drop=FALSE]

+    edge.attr <- cbind(edge.attr, graphics=toGML(edge.graphics, "graphics"))

+    edges.gml <- toGML(edge.attr, "edge", "\n")

+    

+    gml.ret <- paste('Creator "NetPathMiner ', packageVersion("NetPathMiner"), 

+            ' ', date(),'" \n',

+            'graph [\n', nodes.gml, '\n', edges.gml, '\n]',

+            sep='')

+    

+    write(gml.ret, file=file)

+}

 ####################### Internal functions for plotting #######################

 plotNetwork_internal <- function(graph, vertex.color, col.palette = palette(), layout = layout.auto, legend,...){

@@ -731,6 +869,36 @@ drawLegend <- function(vertices, paths){

 } 

+toGML <- function(attr, element, collapse=NULL){

+    l <- length(attr)

+    num <- sapply(attr,is.numeric)

+    

+    # Formats create sprintf formulas for GML formatting.

+    # In general, GML format follow the pattern: attr.name attr.val \n

+    # Attribute string values are quoted.

+    #

+    # Format1 deals with attribute names.

+    # Format2 deals with attribute values.

+    # Format interwines format1 and format2, and supply it to sprintf method.

+    

+    format1 <- paste('%',1:l,'$s ', sep='') # attribute names exported as strings.

+    format1[names(attr)== "graphics"] <- ''

+    format2 <- paste('%', (1:l) + l,'$', ifelse(num, 'g', 's'), sep='')

+    format2 <- paste(ifelse(num, '', '"'), 

+            format2, 

+            ifelse(num, '\n', '"\n'), 

+            sep='')

+    

+    format2[names(attr)== "graphics"] <- paste('%', which(names(attr)== "graphics") + l, '$s\n', sep='')

+    

+    format <- paste(t(cbind(format1, format2)), collapse='')

+    format <- paste(element, '[\n', format, ']')

+    

+    

+    ret <- paste( do.call(function(...) sprintf(format, ...), c(names(attr), attr)) ,collapse=collapse) 

+    return(ret)

+}

+

 strWrap <- function(s, width=20){

     for(i in 1:(floor( max(nchar(s))/ width )+1) ){

         s <- gsub( paste( "(^| |\n)(.{", width-2, ",", width, "}) ", sep=""),"\\1\\2\n", s , perl=TRUE)

@@ -1,3 +1,4 @@

+% Generated by roxygen2 (4.0.1): do not edit by hand

 \name{KGML2igraph}

 \alias{KGML2igraph}

 \title{Processes KGML files into igraph objects}

@@ -6,51 +7,38 @@ 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. If a directory path is provided,

-  all *.xml files in it and its subdirectories are

-  included.}

+\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{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 ignored if

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

+\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.}

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

 }

 \value{

-An igraph object, representing a metbolic or a signaling

-network.

+An igraph object, representing a metbolic or a signaling network.

 }

 \description{

-This function takes KGML files as input, and returns either

-a metabolic or a signaling network as output.

+This function takes KGML files as input, and returns either a metabolic or a signaling

+network as output.

 }

 \details{

-Users can specify whether files are processes as metabolic

-or signaling networks.

+Users can specify whether files are processes as metabolic or signaling networks.

-Metabolic networks are given as bipartite graphs, where

-metabolites and reactions represent vertex types. This is

-constructed from <reaction> xml node in KGML file,

-connecting them to their corresponding substrates and

-products. Each reaction vertex has \code{genes} attribute,

-listing all genes associated with the reaction. As a

-general rule, reactions inherit all annotation attributes

-of its catalyzig genes.

+Metabolic networks are given as bipartite graphs, where metabolites and reactions represent

+vertex types. This is constructed from <reaction> xml node in KGML file, connecting them

+to their corresponding substrates and products. Each reaction vertex has \code{genes} attribute,

+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> and <PCrel> interactions from KGML files.

+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 file in a directory, as

-pass the directory path as \code{filename}

+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}

 }

 \examples{

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

@@ -69,7 +57,7 @@ if(is.loaded("readkgmlfile")){ # This is false if libxml2 wasn't available at in

 Ahmed Mohamed

 }

 \seealso{

-Other Database extraction methods:

-\code{\link{SBML2igraph}}; \code{\link{biopax2igraph}}

+Other Database extraction methods: \code{\link{SBML2igraph}};

+  \code{\link{biopax2igraph}}

 }

@@ -1,3 +1,4 @@

+% Generated by roxygen2 (4.0.1): do not edit by hand

 \name{stdAttrNames}

 \alias{fetchAttribute}

 \alias{stdAttrNames}

@@ -9,38 +10,28 @@ fetchAttribute(graph, organism = "Homo sapiens", target.attr, source.attr,

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

 }

 \arguments{

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

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

-  \item{return.value}{Specify whether to return the names

-  of matched standard annotations, or modify the graph

-  attribute names to match the standards.}

+\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).}

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

-  \item{target.attr}{The target annotation, given as MIRIAM

-  standard in the format \code{miriam.xxx}}

+\item{target.attr}{The target annotation, given as MIRIAM standard in the format \code{miriam.xxx}}

-  \item{source.attr}{The source annotation attribute from

-  \code{graph}}

+\item{source.attr}{The source annotation attribute from \code{graph}}

-  \item{bridge.web}{The base URL for Brigde Database

-  webservices.}

+\item{bridge.web}{The base URL for Brigde Database webservices.}

 }

 \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). \code{graph}

-returns the input graph with attribute names standardized.

+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).

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

-For \code{fetchAttribute}, the input \code{graph} with the

-fetched attribute mapped to vertices.

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

 }

 \description{

-These functions deals with conforming with MIRIAM

-annotation guidelines, conversion and mapping between

-MIRIAM identifiers.

+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

@@ -56,9 +47,8 @@ data(ex_kgml_sig)	# Ras and chemokine signaling pathways in human

 Ahmed Mohamed

 }

 \seealso{

-Other Attribute handling methods:

-\code{\link{getAttrNames}}, \code{\link{getAttrStatus}},

-\code{\link{getAttribute}}, \code{\link{rmAttribute}},

-\code{\link{setAttribute}}

+Other Attribute handling methods: \code{\link{getAttrNames}},

+  \code{\link{getAttrStatus}}, \code{\link{getAttribute}},

+  \code{\link{rmAttribute}}, \code{\link{setAttribute}}

 }

@@ -1,3 +1,4 @@

+% Generated by roxygen2 (4.0.1): do not edit by hand

 \name{NPMdefaults}

 \alias{NPMdefaults}

 \title{Default values for NetPathMiner}

@@ -5,26 +6,22 @@

 NPMdefaults(value)

 }

 \arguments{

-  \item{value}{a character string indicating the variable

-  name.}

+\item{value}{a character string indicating the variable name.}

 }

 \value{

 The defuult value for the given variable.

 }

 \description{

-This function gets a NetPathMiner default value for a

-variable.

+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 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}}. }

+NetPathMiner defines the following defaults:

+\itemize{

+  \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}}.

+}

 }

 \examples{

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

@@ -1,3 +1,4 @@

+% Generated by roxygen2 (4.0.1): do not edit by hand

 \docType{package}

 \name{NetPathMiner-package}

 \alias{NPM}

@@ -5,15 +6,12 @@

 \alias{NetPathMiner-package}

 \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.

-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 thousands of

-output paths.

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

+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

+thousands of output paths.

 }

 \author{

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

@@ -1,3 +1,4 @@

+% Generated by roxygen2 (4.0.1): do not edit by hand

 \name{SBML2igraph}

 \alias{SBML2igraph}

 \title{Processes SBML files into igraph objects}

@@ -6,83 +7,57 @@ 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 is provided,

-  all *.xml and *.sbml files in it and its subdirectories

-  are included.}

+\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.}

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

-  \item{miriam.attr}{A list of annotation attributes to be

-  extracted. If \code{"all"}, then all attibutes 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{miriam.attr}{A list of annotation attributes to be extracted. If \code{"all"}, then all attibutes

+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 representing

-  small molecules (see Details). Ignored if

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

+\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

-  \code{parse.as="metabolic"}, or when \code{gene.attr} is

-  not provided.}

+\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.}

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

 }

 \value{

-An igraph object, representing a metbolic or a signaling

-network.

+An igraph object, representing a metbolic or a signaling network.

 }

 \description{

-This function takes SBML files as input, and returns either

-a metabolic or a signaling network as output.

+This function takes SBML files as input, and returns either a metabolic or a signaling

+network as output.

 }

 \details{

-Users can specify whether files are processes as metabolic

-or signaling networks.

+Users can specify whether files are processes as metabolic or signaling networks.

-Metabolic networks are given as bipartite graphs, where

-metabolites and reactions represent vertex types. This is

-constructed from \code{ListOfReactions} in SBML file,

-connecting them to their corresponding substrates and

-products (\code{ListOfSpecies}). Each reaction vertex has

-\code{genes} attribute, listing all \code{modifiers} of

-this reaction. As a general rule, reactions inherit all

-annotation attributes of its catalyzig genes.

+Metabolic networks are given as bipartite graphs, where metabolites and reactions represent

+vertex types. This is constructed from \code{ListOfReactions} in SBML file, connecting them

+to their corresponding substrates and products (\code{ListOfSpecies}). Each reaction vertex has \code{genes} attribute,

+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 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

-molecules, user can provide \code{gene.attr} (for example:

-\code{miriam.uniprot} or \code{miriam.ncbigene}) where only

-annotated species are considered genes.

+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

+molecules, user can provide \code{gene.attr} (for example: \code{miriam.uniprot} or \code{miriam.ncbigene})

+where only annotated species are considered genes.

-All annotation attributes written according to MIRIAM

-guidlines (either \code{urn:miriam:xxx:xxx} or

-\code{http://identifiers.org/xxx/xxx}) are etxracted by

-default. Non-conforming attributes can be extracted by

-specifying \code{miriam.attr}.

+All annotation attributes written according to MIRIAM guidlines (either \code{urn:miriam:xxx:xxx} or

+\code{http://identifiers.org/xxx/xxx}) are etxracted by default. Non-conforming attributes can be extracted

+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}

+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