## 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)
## Plot paths.
plotPaths(ranked.p, rgraph)
## Convert paths to binary matrix, build a classifier.
ybinpaths <- pathsToBinary(ranked.p)
p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)
## Plotting with clusters, on a metabolic graph.
plotPaths(ranked.p, ex_sbml, path.clusters=p.class)
Run the code above in your browser using DataLab