plotPathCluster {NetPathMiner} | R Documentation |
Plots the structure of specified path found by pathCluster.
plotPathCluster(ybinpaths, clusters, m, tol = NULL)
ybinpaths |
The training paths computed by |
clusters |
The pathway cluster model trained by |
m |
The path cluster to view. |
tol |
A tolerance for 3M parameter |
Produces a plot of the paths with the path probabilities and cluster membership probabilities.
Center Plot |
An image of all paths the training dataset. Rows are the paths and columns are the genes (features) included within each path. |
Right |
The training set posterior probabilities for each path belonging to the current 3M component. |
Top Bar Plots |
|
Timothy Hancock and Ichigaku Takigawa
Other Path clustering & classification methods: pathClassifier
,
pathCluster
, pathsToBinary
,
plotClassifierROC
,
plotClusterMatrix
,
plotPathClassifier
,
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) plotPathCluster(ybinpaths, p.cluster, m=2, tol=0.05)