Description Usage Arguments Value Author(s) References See Also Examples
3M Markov mixture model for clustering pathways
| 1 | pathCluster(ybinpaths, M, iter = 1000)
 | 
| ybinpaths | The training paths computed by  | 
| M | The number of clusters. | 
| iter | The maximum number of EM iterations. | 
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. | 
Ichigaku Takigawa
Timothy Hancock
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.
Other Path clustering & classification methods: pathClassifier,
pathsToBinary,
plotClassifierROC,
plotClusterMatrix,
plotPathClassifier,
plotPathCluster,
predictPathClassifier,
predictPathCluster
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 	## 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)
 
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