plotAllNetworks {NetPathMiner} | R Documentation

## 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.

### Author(s)

Ahmed Mohamed

### See Also

Other Plotting methods: `colorVertexByAttr`, `layoutVertexByAttr`, `plotClassifierROC`, `plotClusterMatrix`, `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)

[Package NetPathMiner version 1.16.0 Index]