plotPaths {NetPathMiner} | R Documentation

## Plots an annotated igraph object higlighting ranked paths.

### Description

This function plots a network highlighting ranked paths. If `path.clusters` are provided, paths in the same cluster are assigned similar colors.

### Usage

    plotPaths(paths, graph, path.clusters = NULL, col.palette = palette(),
      layout = layout.auto, ...)

### Arguments

`paths` | The result of `pathRanker`.
`graph` | An annotated igraph object.
`path.clusters` | The result from `pathCluster` or `pathClassifier`.
`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

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.

### Author(s)

Ahmed Mohamed

### See Also

Other Plotting methods: `colorVertexByAttr`, `layoutVertexByAttr`, `plotAllNetworks`, `plotClassifierROC`, `plotClusterMatrix`, `plotCytoscapeGML`, `plotNetwork`, `plotPathClassifier`

### 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)

    	## 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)

[Package NetPathMiner version 1.20.0 Index]