Predicts new paths given a pathCluster model.

1 | ```
predictPathCluster(pfit, newdata)
``` |

`pfit` |
The pathway cluster model trained by |

`newdata` |
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. |

Ichigaku Takigawa

Timothy Hancock

Other Path clustering & classification methods: `pathClassifier`

,
`pathCluster`

, `pathsToBinary`

,
`plotClassifierROC`

,
`plotClusterMatrix`

,
`plotPathClassifier`

,
`plotPathCluster`

,
`predictPathClassifier`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ```
## 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)
## just an example of how to predict cluster membership.
pclust.pred <- predictPathCluster(p.cluster,ybinpaths$paths)
``` |

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