plotPathCluster {NetPathMiner} | R Documentation

## Plots the structure of specified path cluster

### Description

Plots the structure of specified path found by pathCluster.

### Usage

    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.

### Author(s)

Timothy Hancock and Ichigaku Takigawa

### See Also

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

### 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", 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)

[Package NetPathMiner version 1.30.0 Index]