pathsToBinary {NetPathMiner} | R Documentation

## Converts the result from pathRanker into something suitable for pathClassifier or pathCluster.

### Description

Converts the result from pathRanker into something suitable for pathClassifier or pathCluster.

### Usage

    pathsToBinary(ypaths)

### Arguments

`ypaths` | The result of `pathRanker`.

### Details

Converts a set of pathways from `pathRanker` into a list of binary pathway matrices. If the pathways are grouped by a response label then the pathsToBinary returns a list labeled by response class where each element is the binary pathway matrix for each class. If the pathways are from `pathRanker` then a list wiht a single element containing the binary pathway matrix is returned. To look up the structure of a specific binary path in the corresponding `ypaths` object simply use matrix index by calling `ypaths[[ybinpaths\$pidx[i,]]]`, where `i` is the row in the binary paths object you wish to reference.

### Value

A list with the following elements.

`paths` | All paths within ypaths converted to a binary string and concatenated into the one matrix.
`y` | The response variable.
`pidx` | An matrix where each row specifies the location of that path within the `ypaths` object.

### Author(s)

Timothy Hancock and Ichigaku Takigawa

### See Also

Other Path clustering & classification methods: `pathClassifier`, `pathCluster`, `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",
    		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.cluster <- pathCluster(ybinpaths, M=3)
    	plotClusters(ybinpaths, p.cluster, col=c("red", "green", "blue") )

[Package NetPathMiner version 1.26.0 Index]