netSimAndEval: Generate simulated network and test

In LiLabAtVT/CoReg: Identification of co-regulators

Generate simulated network and test

Description

This function performs generates simulated network(s) and use different module-finding methods to identify modules in the simulated networks. The performance of different methods will be evaluated by calculating NMI score between ground-truth modules and the algorithm identified modules

Usage

netSimAndEval(mSize,mNum,targetNum,auxNum,prob,nRep=1,testMethods=c("coregJac","coregInv","coregGeo","lp","wt","eb"),nThreads=1)
  ## S3 method for class 'CoReg.NetSim'
print(CoReg.NetSim)
  ## S3 method for class 'CoReg.NetSim'
summary(CoReg.NetSim)
  ## S3 method for class 'CoReg.NetSim'
plot(CoReg.NetSim)

Arguments

mSize	Size of module in simulated network(s)
mNum	Number of regulator gene in each module
targetNum	Number of target genes for each regualtor in the module
auxNum	Number of auxiliary genes in simulated network(s)
prob	Co-regulation probability. A vector of numeric values between 0 and 1. Larger value generates network with stronger co-regulation pattern
nRep	Number of replicates for each data point
testMethods	Methods used for finding modules. Their performance will be evaluated by NMI score. Available opotions are: "coregJac" (CoReg + Jaccard index), "coregGeo" (CoReg + geometric index),"coregInv" (CoReg + inverse log-weighted index),"lp" (label propagation),"wt" (walk trap),"eb" (edge betweenness)
nThreads	Number of threads for running the simulation. Only valid when "coregGeo" is selected in Methods
CoReg.NetSim	CoReg.NetSim object

Details

This function first calls generateSimNet() to generate simulated network(s) with pre-specified modular structure, and then runs different module-finding algorithms to identify modules. The correlation between pre-specified modules and algorithm identified modules is calculated using NMI score. The result can be plotted using generic function plot()

Value

An object of class CoReg.NetSim

Author(s)

Qi Song

References

Guimerà R, Sales-Pardo M, Amaral LAN: Module identification in bipartite and directed networks. Phys Rev E - Stat Nonlinear, Soft Matter Phys 2007, 76. Danon L, Díaz-Guilera A, Duch J, Arenas A: Comparing community structure identification. J Stat Mech Theory Exp 2005, 2005:P09008–P09008.

See Also

generateSimNet rewSim

Examples

re<-netSimAndEval(10,5,20,100,0.5,testMethods=c("coregJac","lp","wt","eb"))

# See the summary
summary(re)

# Plot the evaluation result
plot(re)

LiLabAtVT/CoReg documentation built on May 8, 2022, 10:17 a.m.