predictPathCluster {NetPathMiner} | R Documentation

## Predicts new paths given a pathCluster model

### Description

Predicts new paths given a pathCluster model.

### Usage

    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

    	## 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)

[Package NetPathMiner version 1.30.0 Index]