## ----echo=FALSE, results="hide", warning=FALSE-------------------------------- suppressPackageStartupMessages({library('NetPathMiner')}) ## ----Load_package, echo=TRUE, eval=TRUE, results="hide"----------------------- library(NetPathMiner) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # graph <- KGML2igraph(filename = file) # graph <- SBML2igraph(filename = file) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # library(rBiopaxParser) # biopax = readBiopax(file) # graph <- BioPAX2igraph(biopax = biopax) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # graph <- KGML2igraph(filename = c(file1, file2)) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # graph <- KGML2igraph(filename = ".") ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # # Extract all MIRIAM identifiers from an SBML file. # graph <- SBML2igraph(filename = file, miriam = "all") # # # Extract only miram.go identifiers from a BioPAX file. # graph <- BioPAX2igraph(biopax = biopax, miriam = "go") ## ----echo=FALSE, eval=TRUE, results="hide"------------------------------------ file <- file.path(find.package("NetPathMiner"), "extdata", "hsa00860.xml") ## ----echo=TRUE, eval=FALSE, results="hide"------------------------------------ # graph <- KGML2igraph(filename = file, parse.as = "signaling") # # graph <- KGML2igraph(filename = file, parse.as = "signaling", # expand.complexes = TRUE) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- data("ex_sbml") graph <- ex_sbml graph ## ----echo=TRUE, eval=TRUE----------------------------------------------------- head( V(graph) ) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- head( E(graph) ) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- head( V(graph)[ reactions ] ) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- V(graph)[ "reaction_71850" ]$attr ## ----echo=TRUE, eval=TRUE----------------------------------------------------- getAttrNames(graph) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- getAttrStatus(graph, pattern = "^miriam.") ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # require("RCurl") # # Fetch uniprot annotation # graph <- fetchAttribute(graph, organism = "Homo sapiens", target.attr = "miriam.ncbigene" , source.attr = "miriam.uniprot") # # # Fetch ChEBI annotation. # graph <- fetchAttribute(graph, target.attr = "miriam.chebi", source.attr = "miriam.kegg.compound") ## ----echo=TRUE, eval=TRUE----------------------------------------------------- rgraph <- makeReactionNetwork(graph, simplify=FALSE) rgraph ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # rgraph <- simplifyReactionNetwork(rgraph) # rgraph <- makeReactionNetwork(graph, simplify=TRUE) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- # Expand complexes of gene network. ggraph <- expandComplexes(rgraph, v.attr = "miriam.uniprot", keep.parent.attr= c("^pathway", "^compartment")) # Convert reaction network to gene network. ggraph <- makeGeneNetwork(rgraph) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- data(ex_microarray) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # # Assign weights to edges. # if(require("RCurl") && url.exists( NPMdefaults("bridge.web") )) # rgraph <- fetchAttribute(rgraph, organism = "Homo sapiens", # target.attr = "miriam.affy.probeset", # source.attr = "miriam.uniprot") ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # # This requires an internet connection, and RCurl and ALL packages to be present. # # Instead, we will actually use a processed ALL data, where features are converted # # to miriam.uniprot annotation. (Next chunk) # # library(ALL) # data(ALL) # rgraph <- assignEdgeWeights(microarray = exprs(ALL), graph = rgraph, # weight.method = "cor", use.attr="miriam.affy.probeset", y=ALL$mol.bio, bootstrap = FALSE) ## ----echo=FALSE, eval=TRUE---------------------------------------------------- # This is what is evaluated. data(ex_microarray) rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph, weight.method = "cor", use.attr="miriam.uniprot", y=colnames(ex_microarray), bootstrap = FALSE) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- rgraph$y.labels head( E(rgraph)$edge.weights ) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- ranked.p <- pathRanker(rgraph, method = "prob.shortest.path", K = 25, minPathSize = 6) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # pathsample <- samplePaths(rgraph, max.path.length = vcount(rgraph), # num.samples = 1000, num.warmup = 10) # # ranked.p <- pathRanker(rgraph, method = "pvalue", # sampledpaths = pathsample ,alpha=0.1) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- # Get paths as edge IDs. eids <- getPathsAsEIDs(paths = ranked.p, graph = rgraph) ## ----echo=TRUE, eval=TRUE, results="hide"------------------------------------- # Convert paths to other networks. eids <- getPathsAsEIDs(paths = ranked.p, graph = ggraph) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- # Clustering. ybinpaths <- pathsToBinary(ranked.p) p.cluster <- pathCluster(ybinpaths, M = 2) ## ----fig=TRUE, pdf=TRUE, echo=TRUE, eval=TRUE--------------------------------- plotClusters(ybinpaths, p.cluster) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 2) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # plotClassifierROC(p.class) ## ----fig=TRUE, pdf=TRUE, echo=TRUE, eval=TRUE--------------------------------- plotClusters(ybinpaths, p.class) ## ----echo=TRUE, eval=TRUE----------------------------------------------------- plotNetwork(rgraph, vertex.color="compartment.name") ## ----fig=TRUE, pdf=TRUE, echo=TRUE, eval=FALSE-------------------------------- # plotPaths(ranked.p, rgraph) # # # With clusters # plotPaths(ranked.p, graph, path.clusters=p.class) ## ----fig=TRUE, pdf=TRUE, echo=TRUE, eval=TRUE--------------------------------- plotAllNetworks(ranked.p, metabolic.net = graph, reaction.net = rgraph, path.clusters=p.class, vertex.label = "", vertex.size = 4) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # layout.c <- clusterVertexByAttr(rgraph, "pathway", cluster.strength = 3) # v.color <- colorVertexByAttr(rgraph, "pathway") # plotPaths(ranked.p , rgraph, clusters=p.class, # layout = layout.c, vertex.color = v.color) ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # plotCytoscapeGML(graph, file="example.gml", layout = layout.c, # vertex.size = 5, vertex.color = v.color) ## ----echo=TRUE, eval=TRUE, results="hide"------------------------------------- getGeneSets(graph, use.attr="compartment", gene.attr="miriam.uniprot") ## ----echo=TRUE, eval=TRUE, results="hide"------------------------------------- getGeneSetNetworks(graph, use.attr="compartment") ## ----echo=TRUE, eval=FALSE---------------------------------------------------- # graphNEL <- toGraphNEL(graph, export.attr="^miriam.")