plotPBNTransitions: Visualize the transitions in a probabilistic Boolean network

Description

Visualizes the state transitions and their probabilities in a probabilistic Boolean network. This takes the transition table information calculated by the markovSimulation method. Only transitions with non-zero probability are included in the plot. The function requires the igraph package.

Usage

plotPBNTransitions(markovSimulation, 
                   stateSubset, 
                   drawProbabilities = TRUE, 
                   drawStateLabels = TRUE, 
                   layout = layout.fruchterman.reingold, 
                   plotIt = TRUE, ...)

Arguments

markovSimulation

An object of class MarkovSimulation, as returned by markovSimulation. As the transition table information in this structure is required,

stateSubset

An optional list of states, where each element of the list must be a vector with a 0/1 entry for each gene. If this argument is supplied, the graph only contains the specified states and transitions between these states.

drawProbabilities

If set to true, the edges of the graph are annotated with the probabilities of the corresponding transitions. Default is TRUE.

drawStateLabels

If set to true, the vertices of the graph are annotated with the gene values of the corresponding states. Defaults to TRUE.

layout

A layouting function that determines the placement of the nodes in the graph. Please refer to the layout manual entry in the igraph package for further details. By default, the Fruchterman-R

plotIt

If this is true, a plot is generated. Otherwise, only an object of class igraph is returned, but no plot is drawn.

...

Further graphical parameters to be passed to plot.igraph.

Value

Returns an invisible object of class igraph containing the wiring graph.

Details

This function uses the plot.igraph function from the igraph package. The plots are customizeable using the ... argument. For details on possible parameters, please refer to igraph.plotting.

Examples

Run this code

library(BoolNet)

# load example network
data(examplePBN)

# perform a Markov chain simulation
sim <- markovSimulation(examplePBN)

# plot the transitions and their probabilities
plotPBNTransitions(sim)