NetPathMiner (version 1.8.0)

plotAllNetworks: Higlighting ranked paths over multiple network representations.

Description

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.

Usage

plotAllNetworks(paths, metabolic.net = NULL, reaction.net = NULL, gene.net = NULL, path.clusters = NULL, plot.clusters = TRUE, col.palette = palette(), layout = layout.auto, ...)

Arguments

paths

The result of pathRanker.

metabolic.net

A bipartite metabolic network.

reaction.net

A reaction network, resulting from makeReactionNetwork.

gene.net

A gene network, resulting from makeGeneNetwork.

path.clusters

The result from pathCluster or pathClassifier.

plot.clusters

Whether to plot clustering information, as generated by plotClusters

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 plotNetwork.

Value

Highlights the path list over all provided networks.

See Also

Other Plotting methods: colorVertexByAttr; layoutVertexByAttr; plotClassifierROC; plotClusterMatrix, plotClusterProbs, plotClusters; plotCytoscape, plotCytoscapeGML; plotNetwork; plotPathClassifier; plotPaths

Examples

## 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)