pathCluster {NetPathMiner} | R Documentation |
3M Markov mixture model for clustering pathways
pathCluster(ybinpaths, M, iter = 1000)
ybinpaths |
The training paths computed by |
M |
The number of clusters. |
iter |
The maximum number of EM iterations. |
A list with the following items:
h |
The posterior probabilities that each path belongs to each cluster. |
labels |
The cluster membership labels. |
theta |
The probabilities of each gene for each cluster. |
proportions |
The mixing proportions of each path. |
likelihood |
The likelihood convergence history. |
params |
The specific parameters used. |
Ichigaku Takigawa
Timothy Hancock
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.
Other Path clustering & classification methods: pathClassifier
,
pathsToBinary
,
plotClassifierROC
,
plotClusterMatrix
,
plotPathClassifier
,
plotPathCluster
,
predictPathClassifier
,
predictPathCluster
## 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)