plotAllNetworks {NetPathMiner} | R Documentation |
This function highlighting ranked paths over different network representations, metabolic, reaction and gene networks. The functions finds equivalent paths across different networks and marks them.
plotAllNetworks(paths, metabolic.net = NULL, reaction.net = NULL, gene.net = NULL, path.clusters = NULL, plot.clusters = TRUE, col.palette = palette(), layout = layout.auto, ...)
paths |
The result of |
metabolic.net |
A bipartite metabolic network. |
reaction.net |
A reaction network, resulting from |
gene.net |
A gene network, resulting from |
path.clusters |
The result from |
plot.clusters |
Whether to plot clustering information, as generated by |
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 |
Highlights the path list over all provided networks.
Ahmed Mohamed
Other Plotting methods: colorVertexByAttr
,
layoutVertexByAttr
,
plotClassifierROC
,
plotClusterMatrix
,
plotCytoscapeGML
,
plotNetwork
,
plotPathClassifier
, plotPaths
## 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) plotAllNetworks(ranked.p, metabolic.net = ex_sbml, reaction.net = rgraph, vertex.label = "", vertex.size = 4)