pathCluster {NetPathMiner} | R Documentation

## 3M Markov mixture model for clustering pathways

### Description

3M Markov mixture model for clustering pathways

### Usage

    pathCluster(ybinpaths, M, iter = 1000)

### Arguments

`ybinpaths` | The training paths computed by `pathsToBinary`.
`M` | The number of clusters.
`iter` | The maximum number of EM iterations.

### Value

A list with the following items:

`h` | The posterior probabilities that each path belongs to each cluster.
`labels` | The cluster membership labels.
`theta` | The probabilities of each gene for each cluster.
`proportions` | The mixing proportions of each path.
`likelihood` | The likelihood convergence history.
`params` | The specific parameters used.

### Author(s)

Ichigaku Takigawa

Timothy Hancock

### References

Mamitsuka, H., Okuno, Y., and Yamaguchi, A. 2003. Mining biologically active patterns in metabolic pathways using microarray expression profiles. SIGKDD Explor. News l. 5, 2 (Dec. 2003), 113-121.

### See Also

Other Path clustering & classification methods: `pathClassifier`, `pathsToBinary`, `plotClassifierROC`, `plotClusterMatrix`, `plotPathClassifier`, `plotPathCluster`, `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)
    	plotClusters(ybinpaths, p.cluster)

[Package NetPathMiner version 1.30.0 Index]