plotPathClassifier {NetPathMiner} | R Documentation

## Plots the structure of specified path found by pathClassifier.

### Description

Plots the structure of specified path found by pathClassifier.

### Usage

    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

    	## 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)

[Package NetPathMiner version 1.30.0 Index]