Predicts new paths given a pathCluster model.
predictPathCluster(pfit, newdata)The pathway cluster model trained by pathCluster or pathClassifier.
The binary pathway dataset to be assigned a cluster label.
A list with the following elements:
labels | a vector indicating the 3M cluster membership. |
posterior.probs | a matrix of posterior probabilities for each path belonging to each cluster. |
Other Path clustering & classification methods:
pathClassifier(),
pathCluster(),
pathsToBinary(),
plotClassifierROC(),
plotClusterMatrix(),
plotPathClassifier(),
plotPathCluster(),
predictPathClassifier()
## 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", 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.
## Get ranked paths using probabilistic shortest paths.
ranked.p <- pathRanker(rgraph, method="prob.shortest.path",
K=20, minPathSize=8)
#> Extracting the 20 most probable paths.
## Convert paths to binary matrix.
ybinpaths <- pathsToBinary(ranked.p)
p.cluster <- pathCluster(ybinpaths, M=2)
## just an example of how to predict cluster membership.
pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)