## 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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(NetPathMiner)
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/NetPathMiner/plotPaths.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotPaths
> ### Title: Plots an annotated igraph object higlighting ranked paths.
> ### Aliases: plotPaths
>
> ### ** 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)
>
> ## 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
>
> ## 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)
>
>
>
>
>
> dev.off()
null device
1
>