Plots the structure of all path clusters

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

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; plotCytoscape, 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") )

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(NetPathMiner)
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/NetPathMiner/plotClusters.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotClusterMatrix
> ### Title: Plots the structure of all path clusters
> ### Aliases: plotClusterMatrix plotClusterProbs plotClusters
> 
> ### ** 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)
100 genes were present in the microarray, but not represented in the network.
55 genes were couldn't be found in microarray.
Assigning edge weights for label ALL1/AF4 
Assigning edge weights for label BCR/ABL 
Assigning edge weights for label E2A/PBX1 
Assigning edge weights for label NEG 
> 
> 	## Get ranked paths using probabilistic shortest paths.
>  ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
+ 					K=20, minPathSize=8)
Extracting the 20 most probable paths for ALL1/AF4
Extracting the 20 most probable paths for BCR/ABL
Extracting the 20 most probable paths for E2A/PBX1
Extracting the 20 most probable paths for NEG
> 
> 	## Convert paths to binary matrix.
> 	ybinpaths <- pathsToBinary(ranked.p)
> 	p.cluster <- pathCluster(ybinpaths, M=2)
> 	plotClusters(ybinpaths, p.cluster, col=c("red", "blue") )
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>