plotClusters: Plots the structure of all path clusters

In aiminy/NetPathMiner: NetPathMiner for Biological Network Construction, Path Mining and Visualization

Description

Plots the structure of all path clusters

Usage

plotClusterMatrix(ybinpaths, clusters, col = rainbow(clusters$params$M),
  grid = TRUE)

plotClusterProbs(clusters, col = rainbow(clusters$params$M))

plotClusters(ybinpaths, clusters, col, ...)

Arguments

ybinpaths	The training paths computed by pathsToBinary.
clusters	The pathway cluster model trained by pathCluster or pathClassifier.
col	Colors for each path cluster.
grid	A logical, whether to add a grid to the plot
...	Extra paramaters passed to plotClusterMatrix

Value

plotClusterMatrix plots an image of all paths the training dataset. Rows are the paths and columns are the genes (features) included within each path. Paths are colored according to cluster membership.

plotClusterProbs The training set posterior probabilities for each path belonging to a 3M component.

plotClusters: combines the two plots produced by plotClusterProbs and plotClusterMatrix.

Author(s)

Ahmed Mohamed

See Also

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

Other Plotting methods: colorVertexByAttr, layoutVertexByAttr, plotAllNetworks, plotClassifierROC, plotCytoscapeGML, plotNetwork, plotPathClassifier, plotPaths

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=8)
	
	## Convert paths to binary matrix. 
	ybinpaths <- pathsToBinary(ranked.p)
	p.cluster <- pathCluster(ybinpaths, M=2)
	plotClusters(ybinpaths, p.cluster, col=c("red", "blue") )
 

aiminy/NetPathMiner documentation built on May 12, 2019, 3:38 a.m.