plotPaths {NetPathMiner} | R Documentation |
This function plots a network highlighting ranked paths. If path.clusters
are provided,
paths in the same cluster are assigned similar colors.
plotPaths(paths, graph, path.clusters = NULL, col.palette = palette(), layout = layout.auto, ...)
paths |
The result of |
graph |
An annotated igraph object. |
path.clusters |
The result from |
col.palette |
A color palette, or a palette generating function (ex: col.palette=rainbow ). |
layout |
Either a graph layout function, or a two-column matrix specifiying vertex coordinates. |
... |
Additional arguments passed to |
Produces a plot of the network with paths highlighted. If paths are computed for several labels (sample categories), a plot is created for each label.
Ahmed Mohamed
Other Plotting methods: colorVertexByAttr
,
layoutVertexByAttr
,
plotAllNetworks
,
plotClassifierROC
,
plotClusterMatrix
,
plotCytoscapeGML
,
plotNetwork
,
plotPathClassifier
## Prepare a weighted reaction network. ## Conver a metabolic network to a reaction network. data(ex_sbml) # bipartite metabolic network of Carbohydrate metabolism. rgraph <- makeReactionNetwork(ex_sbml, simplify=TRUE) ## Assign edge weights based on Affymetrix attributes and microarray dataset. # Calculate Pearson's correlation. data(ex_microarray) # Part of ALL dataset. rgraph <- assignEdgeWeights(microarray = ex_microarray, graph = rgraph, weight.method = "cor", use.attr="miriam.uniprot", y=factor(colnames(ex_microarray)), bootstrap = FALSE) ## Get ranked paths using probabilistic shortest paths. ranked.p <- pathRanker(rgraph, method="prob.shortest.path", K=20, minPathSize=6) ## Plot paths. plotPaths(ranked.p, rgraph) ## Convert paths to binary matrix, build a classifier. ybinpaths <- pathsToBinary(ranked.p) p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3) ## Plotting with clusters, on a metabolic graph. plotPaths(ranked.p, ex_sbml, path.clusters=p.class)