View source: R/pathClassifier.R
predictPathClassifier | R Documentation |
Predicts new paths given a pathClassifier model.
predictPathClassifier(mix, newdata)
mix |
The result from |
newdata |
A data.frame containing the new paths to be classified. |
A list with the following elements.
h |
The posterior probabilities for each HME3M component. |
posterior.probs |
The posterior probabilities for HME3M model to classify the response. |
label |
A vector indicating the HME3M cluster membership. |
component |
The HME3M component membership for each pathway. |
path.probabilities |
The 3M path probabilities. |
plr.probabilities |
The PLR predictions for each component. |
Timothy Hancock and Ichigaku Takigawa
Other Path clustering & classification methods:
pathClassifier()
,
pathCluster()
,
pathsToBinary()
,
plotClassifierROC()
,
plotClusterMatrix()
,
plotPathClassifier()
,
plotPathCluster()
,
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",
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)
## Just an example of how to predict cluster membership
pclass.pred <- predictPathCluster(p.class, ybinpaths$paths)
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