Predicts new paths given a pathCluster model

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Description

Predicts new paths given a pathCluster model.

Usage

1
predictPathCluster(pfit, newdata)

Arguments

pfit

The pathway cluster model trained by pathCluster or pathClassifier.

newdata

The binary pathway dataset to be assigned a cluster label.

Value

A list with the following elements:

labels a vector indicating the 3M cluster membership.
posterior.probs a matrix of posterior probabilities for each path belonging to each cluster.

Author(s)

Ichigaku Takigawa

Timothy Hancock

See Also

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

Examples

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	## 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)

	## just an example of how to predict cluster membership.
	pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)
	

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