Plots the structure of specified path cluster

Plots the structure of specified path found by pathCluster.

plotPathCluster(ybinpaths, clusters, m, tol = NULL)

Arguments

ybinpaths

The training paths computed by pathsToBinary.

clusters

The pathway cluster model trained by pathCluster or pathClassifier.

m

The path cluster to view.

tol

A tolerance for 3M parameter theta which is the probability for each edge within each cluster. If the tolerance is set all edges with a theta below that tolerance will be removed from the plot.

Value

Produces a plot of the paths with the path probabilities and cluster membership probabilities.

Center Plot

An image of all paths the training dataset. Rows are the paths and columns are the genes (features) included within each path.

Right

The training set posterior probabilities for each path belonging to the current 3M component.

Top Bar Plots

Theta, The 3M component probabilities - indicates the importance of each edge to a pathway.

See also

Other Path clustering & classification methods: pathClassifier(), pathCluster(), pathsToBinary(), plotClassifierROC(), plotClusterMatrix(), plotPathClassifier(), predictPathClassifier(), predictPathCluster()

Author

Timothy Hancock and Ichigaku Takigawa

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", 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.

  ## Get ranked paths using probabilistic shortest paths.
 ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
          K=20, minPathSize=8)
#> Extracting the 20 most probable paths.

  ## Convert paths to binary matrix.
  ybinpaths <- pathsToBinary(ranked.p)
  p.cluster <- pathCluster(ybinpaths, M=2)
  plotPathCluster(ybinpaths, p.cluster, m=2, tol=0.05)