3M Markov mixture model for clustering pathways

Description

3M Markov mixture model for clustering pathways

Usage

pathCluster(ybinpaths, M, iter = 1000)

Arguments

Value

A list with the following items:

Author(s)

Ichigaku Takigawa

Timothy Hancock

References

Mamitsuka, H., Okuno, Y., and Yamaguchi, A. 2003. Mining biologically active patterns in metabolic pathways using microarray expression profiles. SIGKDD Explor. News l. 5, 2 (Dec. 2003), 113-121.

See Also

Other Path clustering & classification methods: pathClassifier; pathsToBinary; 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", 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)

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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> 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/pathCluster.Rd_%03d_medium.png", width=480, height=480)
> ### Name: pathCluster
> ### Title: 3M Markov mixture model for clustering pathways
> ### Aliases: pathCluster
> 
> ### ** 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)
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.
> 
> 	## 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.
> 
> 	## Convert paths to binary matrix.
> 	ybinpaths <- pathsToBinary(ranked.p)
> 	p.cluster <- pathCluster(ybinpaths, M=2)
> 	plotClusters(ybinpaths, p.cluster)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>