Plots the structure of specified path found by pathCluster.
1  plotPathCluster(ybinpaths, clusters, m, tol = NULL)

ybinpaths 
The training paths computed by 
clusters 
The pathway cluster model trained by 
m 
The path cluster to view. 
tol 
A tolerance for 3M parameter 
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 

Timothy Hancock and Ichigaku Takigawa
Other Path clustering & classification methods: pathClassifier
,
pathCluster
, pathsToBinary
,
plotClassifierROC
,
plotClusterMatrix
,
plotPathClassifier
,
predictPathClassifier
,
predictPathCluster
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  ## 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", bootstrap = FALSE)
## Get ranked paths using probabilistic shortest paths.
ranked.p < pathRanker(rgraph, method="prob.shortest.path",
K=20, minPathSize=8)
## Convert paths to binary matrix.
ybinpaths < pathsToBinary(ranked.p)
p.cluster < pathCluster(ybinpaths, M=2)
plotPathCluster(ybinpaths, p.cluster, m=2, tol=0.05)

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