NetPathMiner (version 1.8.0)

pathsToBinary: 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.

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.

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.

See Also

Other Path clustering & classification methods: pathClassifier; pathCluster; plotClassifierROC; plotClusterMatrix, plotClusterProbs, plotClusters; 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") )