createRBNs: Create a set of random networks.

In csclab/RMut: RMut: a collective framework for mutations analysis in network dynamics

Description

Create a set of random networks using different generation models.

Usage

createRBNs(prexName = "RBN_", numNetworks = 10, model = "BA",
           numNodes = 10, numEdges = 20, probOfNegative = 0.5,
           referedNetwork = NULL, shuffleRate = 4.0)

Arguments

prexName	The common prefix of generated random network names, default is "RBN_"
numNetworks	The number of generated random networks, default is 10
model	The specified generation model from among four models: BA, ER, Shuffle 1 and Shuffle 2. Default is "BA".
numNodes	The number of nodes in a random network, default is 10
numEdges	The number of edges in a random network, default is 20
probOfNegative	The probability of negative links's assignment in a random network, default is 0.5
referedNetwork	The specific reference network used for two shuffling models
shuffleRate	The shuffling intensity of "Shuffle 2" model. The number of rewiring steps = (Shuffling intensity) x (number of edges). Default is 4.

Details

This function generates a set of random networks using a generation model from among four models: Barabasi-Albert (BA) model [1], Erdos-Renyi (ER) variant model [2] and two shuffling models (Shuffle 1 and Shuffle 2) [3]. Refer to the literature in the References section for more details.

Value

The generated random network objects.

References

1. Barabasi A-L, Albert R (1999) Emergence of Scaling in Random Networks. Science 286: 509-512. doi: 10.1126/science.286.5439.509

2. Le D-H, Kwon Y-K (2011) NetDS: A Cytoscape plugin to analyze the robustness of dynamics and feedforward/feedback loop structures of biological networks. Bioinformatics.

3. Trinh H-C, Le D-H, Kwon Y-K (2014) PANET: A GPU-Based Tool for Fast Parallel Analysis of Robustness Dynamics and Feed-Forward/Feedback Loop Structures in Large-Scale Biological Networks. PLoS ONE 9: e103010.

See Also

loadNetwork, calSensitivity, generateStates, generateState, generateGroups, generateGroup, findFBLs, findFFLs, calCentrality

Examples

# Generate all possible initial-states each containing 10 Boolean nodes
set1 <- generateStates(10, "all")

# Generate random networks based on BA model
ba_rbns <- createRBNs("BA_RBN_", 2, "BA", 10, 17)

# For each random network, generate all possible groups each containing a single node
ba_rbns <- generateGroups(ba_rbns, "all", 1, 0)

# For each random network, calculate sensitivity values of all nodes against "knockout" mutation
ba_rbns <- calSensitivity(ba_rbns, set1, "knockout")

# For each random network, calculate structural measures of all nodes/edges
ba_rbns <- findFBLs(ba_rbns, maxLength = 10)
ba_rbns <- findFFLs(ba_rbns)
ba_rbns <- calCentrality(ba_rbns)

print(ba_rbns)
output(ba_rbns)

csclab/RMut documentation built on May 14, 2019, 12:07 p.m.