Higlighting ranked paths over multiple network representations.

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,
  ...
)

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(), plotCytoscapeGML(), plotNetwork(), plotPathClassifier(), plotPaths()

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

plotAllNetworks(ranked.p, metabolic.net = ex_sbml, reaction.net = rgraph,
          vertex.label = "", vertex.size = 4)