pathCluster | 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)
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