perturbTrajectories {BoolNet} | R Documentation |
Perturbs the state trajectories of a network and assesses the robustness by comparing the successor states or the attractors of a set of initial states and a set of perturbed copies of these initial states.
perturbTrajectories(network, measure = c("hamming", "sensitivity", "attractor"), numSamples = 1000, flipBits = 1, updateType = c("synchronous", "asynchronous", "probabilistic"), gene, ...)
network |
A network structure of class |
measure |
Defines the way the robustness is measured (see Details). |
numSamples |
The number of randomly generated pairs of initial states and perturbed copies. Defaults to 1000. |
flipBits |
The number of bits that are flipped to generate a perturbed copy of an initial state. Defaults to 1. |
updateType |
If |
gene |
If |
... |
Further parameters to |
The function generates a set of numSamples
initial states and then applies flipBits
random bit flips to each initial state to generate a perturbed copy of each initial state. For each pair of initial state and perturbed state, a robustness statistic is calculated depending measure
:
If measure="hamming"
, the normalized Hamming distances between the successor states of each initial state and the corresponding perturbed state are calculated.
If measure="sensitivity"
, the average sensitivity of a specific transition function (specified in the gene
parameter) is approximated: The statistic is a logical vector that is TRUE
if gene
differs in the successor states of each initial state and the corresponding perturbed state.
If measure="attractor"
, the attractors of all initial states and all perturbed states are identified. The statistic is a logical vector specifying whether the attractors are identical in each pair of initial state and perturbed initial state.
A list with the following items:
stat |
A vector of size |
value |
The summarized statistic (i.e. the mean value) over all state pairs. |
I. Shmulevich and S. A. Kauffman (2004), Activities and Sensitivities in Boolean Network Models. Physical Review Letters 93(4):048701.
testNetworkProperties
, perturbNetwork
data(cellcycle) # calculate average normalized Hamming distance of successor states hamming <- perturbTrajectories(cellcycle, measure="hamming", numSamples=100) print(hamming$value) # calculate average sensitivity of transition function for gene "Cdh1" sensitivity <- perturbTrajectories(cellcycle, measure="sensitivity", numSamples=100, gene="Cdh1") print(sensitivity$value) # calculate percentage of equal attractors for state pairs attrEqual <- perturbTrajectories(cellcycle, measure="attractor", numSamples=100) print(attrEqual$value)