Converts the result from pathRanker into something suitable for pathClassifier or pathCluster.

Converts the result from pathRanker into something suitable for pathClassifier or pathCluster.

pathsToBinary(ypaths)

Arguments

ypaths

The result of pathRanker.

Value

A list with the following elements.

paths

All paths within ypaths converted to a binary string and concatenated into the one matrix.

y

The response variable.

pidx

An matrix where each row specifies the location of that path within the ypaths object.

Details

Converts a set of pathways from pathRanker into a list of binary pathway matrices. If the pathways are grouped by a response label then the pathsToBinary returns a list labeled by response class where each element is the binary pathway matrix for each class. If the pathways are from pathRanker then a list wiht a single element containing the binary pathway matrix is returned. To look up the structure of a specific binary path in the corresponding ypaths object simply use matrix index by calling ypaths[[ybinpaths\$pidx[i,]]], where i is the row in the binary paths object you wish to reference.

See also

Other Path clustering & classification methods: pathClassifier(), pathCluster(), plotClassifierROC(), plotClusterMatrix(), plotPathClassifier(), plotPathCluster(), predictPathClassifier(), 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.cluster <- pathCluster(ybinpaths, M=3)
  plotClusters(ybinpaths, p.cluster, col=c("red", "green", "blue") )