Predicts new paths given a pathClassifier model.

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Description

Predicts new paths given a pathClassifier model.

Usage

1
predictPathClassifier(mix, newdata)

Arguments

mix

The result from pathClassifier.

newdata

A data.frame containing the new paths to be classified.

Value

A list with the following elements.

h

The posterior probabilities for each HME3M component.

posterior.probs

The posterior probabilities for HME3M model to classify the response.

label

A vector indicating the HME3M cluster membership.

component

The HME3M component membership for each pathway.

path.probabilities

The 3M path probabilities.

plr.probabilities

The PLR predictions for each component.

Author(s)

Timothy Hancock and Ichigaku Takigawa

See Also

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

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", 
		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=6)
	
	## Convert paths to binary matrix. 
	ybinpaths <- pathsToBinary(ranked.p)
	p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)

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

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