Plots an annotated igraph object higlighting ranked paths.

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,
  ...
)

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.

See also

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

Author

Ahmed Mohamed

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)
#> This graph was created by an old(er) igraph version.
#> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version.
#> For now we convert it on the fly...

  ## 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)
#> 100 genes were present in the microarray, but not represented in the network.
#> 55 genes were couldn't be found in microarray.
#> Assigning edge weights for label ALL1/AF4 
#> Assigning edge weights for label BCR/ABL 
#> Assigning edge weights for label E2A/PBX1 
#> Assigning edge weights for label NEG 

  ## Get ranked paths using probabilistic shortest paths.
 ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
          K=20, minPathSize=6)
#> Extracting the 20 most probable paths for ALL1/AF4
#> Extracting the 20 most probable paths for BCR/ABL
#> Extracting the 20 most probable paths for E2A/PBX1
#> Extracting the 20 most probable paths for NEG

  ## 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)