plotClusterMatrix {NetPathMiner} | R Documentation

## Plots the structure of all path clusters

### Description

Plots the structure of all path clusters

### Usage

    plotClusterMatrix(ybinpaths, clusters, col = rainbow(clusters$params$M),
      grid = TRUE)

    plotClusterProbs(clusters, col = rainbow(clusters$params$M))

    plotClusters(ybinpaths, clusters, col, ...)

### Arguments

`ybinpaths` | The training paths computed by `pathsToBinary`.
`clusters` | The pathway cluster model trained by `pathCluster` or `pathClassifier`.
`col` | Colors for each path cluster.
`grid` | A logical, whether to add a `grid` to the plot
`...` | Extra paramaters passed to `plotClusterMatrix`

### Value

`plotClusterMatrix` plots an image of all paths the training dataset. Rows are the paths and columns are the genes (features) included within each path. Paths are colored according to cluster membership.

`plotClusterProbs` The training set posterior probabilities for each path belonging to a 3M component.

`plotClusters`: combines the two plots produced by `plotClusterProbs` and `plotClusterMatrix`.

### Author(s)

Ahmed Mohamed

### See Also

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

Other Plotting methods: `colorVertexByAttr`, `layoutVertexByAttr`, `plotAllNetworks`, `plotClassifierROC`, `plotCytoscapeGML`, `plotNetwork`, `plotPathClassifier`, `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=8)

    	## Convert paths to binary matrix.
    	ybinpaths <- pathsToBinary(ranked.p)
    	p.cluster <- pathCluster(ybinpaths, M=2)
    	plotClusters(ybinpaths, p.cluster, col=c("red", "blue") )

[Package NetPathMiner version 1.28.0 Index]