R: Plots the structure of specified path found by...
plotPathClassifier
R Documentation
Plots the structure of specified path found by pathClassifier.
Description
Plots the structure of specified path found by pathClassifier.
Usage
plotPathClassifier(ybinpaths, obj, m, tol = NULL)
Arguments
ybinpaths
The training paths computed by pathsToBinary
obj
The pathClassifier pathClassifier.
m
The path component to view.
tol
A tolerance for 3M parameter theta which is the probability for each edge within each cluster.
If the tolerance is set all edges with a theta below that tolerance will be removed from the plot.
Value
Produces a plot of the paths with the path probabilities and prediction probabilities and ROC curve overlayed.
Center Plot
An image of all paths the training dataset. Rows are the paths and columns are the genes (vertices)
included within each pathway. A colour within image indicates if a particular gene (vertex) is included within a specific path.
Colours flag whether a path belongs to the current HME3M component (P > 0.5).
Center Right
The training set posterior probabilities for each path belonging to the current 3M component.
Center Top
The ROC curve for this HME3M component.
Top Bar Plots
Theta: The 3M component probabilities - indicates the importance of each edge is to a path.
Beta: The PLR coefficient - the magnitude indicates the importance of the edge to the classify the response.
## 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)
## Convert paths to binary matrix.
ybinpaths <- pathsToBinary(ranked.p)
p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)
## Plotting the classifier results.
plotClassifierROC(p.class)
plotClusters(ybinpaths, p.class)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> 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/plotPathClassifier.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotPathClassifier
> ### Title: Plots the structure of specified path found by pathClassifier.
> ### Aliases: plotPathClassifier
>
> ### ** 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
>
> ## Convert paths to binary matrix.
> ybinpaths <- pathsToBinary(ranked.p)
> p.class <- pathClassifier(ybinpaths, target.class = "BCR/ABL", M = 3)
>
> ## Plotting the classifier results.
> plotClassifierROC(p.class)
> plotClusters(ybinpaths, p.class)
>
>
>
>
>
> dev.off()
null device
1
>