Predicts new paths given a pathClassifier model.

Predicts new paths given a pathClassifier model.

predictPathClassifier(mix, newdata)

Arguments

mix

The result from pathClassifier.

newdata

A data.frame containing the new paths to be classified.

Value

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.

See also

Other Path clustering & classification methods: pathClassifier(), pathCluster(), pathsToBinary(), plotClassifierROC(), plotClusterMatrix(), plotPathClassifier(), plotPathCluster(), predictPathCluster()

Author

Timothy Hancock and Ichigaku Takigawa

Examples

  ## 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)
#> This graph was created by an old(er) igraph version.
#> ℹ Call `igraph::upgrade_graph()` on it to use with the current igraph version.
#> For now we convert it on the fly...

  ## 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)
#> 100 genes were present in the microarray, but not represented in the network.
#> 55 genes were couldn't be found in microarray.
#> Assigning edge weights for label ALL1/AF4 
#> Assigning edge weights for label BCR/ABL 
#> Assigning edge weights for label E2A/PBX1 
#> Assigning edge weights for label NEG 

  ## Get ranked paths using probabilistic shortest paths.
 ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
          K=20, minPathSize=6)
#> Extracting the 20 most probable paths for ALL1/AF4
#> Extracting the 20 most probable paths for BCR/ABL
#> Extracting the 20 most probable paths for E2A/PBX1
#> Extracting the 20 most probable paths for NEG

  ## 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)