Plots the structure of specified path found by pathClassifier.

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Description

Plots the structure of specified path found by pathClassifier.

Usage

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plotPathClassifier(ybinpaths, obj, m, tol = NULL)

Arguments

ybinpaths

The training paths computed by pathsToBinary

obj

The pathClassifier pathClassifier.

m

The path component 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 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

Theta: The 3M component probabilities - indicates the importance of each edge is to a path. Beta: The PLR coefficient - the magnitude indicates the importance of the edge to the classify the response.

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

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	## 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)

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