Plots the structure of specified path found by pathClassifier.
Description
Plots the structure of specified path found by pathClassifier.
Usage
1  plotPathClassifier(ybinpaths, obj, m, tol = NULL)

Arguments
ybinpaths 
The training paths computed by 
obj 
The pathClassifier 
m 
The path component to view. 
tol 
A tolerance for 3M parameter 
Value
Produces a plot of the paths with the path probabilities and prediction probabilities and ROC curve overlayed.
Center Plot 
An image of all paths the training dataset. Rows are the paths and columns are the genes (vertices) included within each pathway. A colour within image indicates if a particular gene (vertex) is included within a specific path. Colours flag whether a path belongs to the current HME3M component (P > 0.5). 
Center Right 
The training set posterior probabilities for each path belonging to the current 3M component. 
Center Top 
The ROC curve for this HME3M component. 
Top Bar Plots 

Author(s)
Timothy Hancock and Ichigaku Takigawa
See Also
Other Path clustering & classification methods: pathClassifier
,
pathCluster
, pathsToBinary
,
plotClassifierROC
,
plotClusterMatrix
,
plotPathCluster
,
predictPathClassifier
,
predictPathCluster
Other Plotting methods: colorVertexByAttr
,
layoutVertexByAttr
,
plotAllNetworks
,
plotClassifierROC
,
plotClusterMatrix
,
plotCytoscapeGML
,
plotNetwork
, plotPaths
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  ## 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)
## Convert paths to binary matrix.
ybinpaths < pathsToBinary(ranked.p)
p.class < pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)
## Plotting the classifier results.
plotClassifierROC(p.class)
plotClusters(ybinpaths, p.class)
