plotPathClassifier {NetPathMiner} | R Documentation |
Plots the structure of specified path found by pathClassifier.
plotPathClassifier(ybinpaths, obj, m, tol = NULL)
ybinpaths |
The training paths computed by |
obj |
The pathClassifier |
m |
The path component to view. |
tol |
A tolerance for 3M parameter |
Produces a plot of the paths with the path probabilities and prediction probabilities and ROC curve overlayed.
Center Plot |
An image of all paths the training dataset. Rows are the paths and columns are the genes (vertices) included within each pathway. A colour within image indicates if a particular gene (vertex) is included within a specific path. Colours flag whether a path belongs to the current HME3M component (P > 0.5). |
Center Right |
The training set posterior probabilities for each path belonging to the current 3M component. |
Center Top |
The ROC curve for this HME3M component. |
Top Bar Plots |
|
Timothy Hancock and Ichigaku Takigawa
Other Path clustering & classification methods: pathClassifier
,
pathCluster
, pathsToBinary
,
plotClassifierROC
,
plotClusterMatrix
,
plotPathCluster
,
predictPathClassifier
,
predictPathCluster
Other Plotting methods: colorVertexByAttr
,
layoutVertexByAttr
,
plotAllNetworks
,
plotClassifierROC
,
plotClusterMatrix
,
plotCytoscapeGML
,
plotNetwork
, plotPaths
## 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=6) ## Convert paths to binary matrix. ybinpaths <- pathsToBinary(ranked.p) p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3) ## Plotting the classifier results. plotClassifierROC(p.class) plotClusters(ybinpaths, p.class)