Some basic criteria of comparison between actual and inferred network.
Allows comparison between actual and inferred network.
## S4 method for signature 'network,network,numeric' compare(Net, Net_inf, nv = 1)
Net |
A network object containing the actual network. |
Net_inf |
A network object containing the inferred network. |
nv |
A number that indicates at which level of cutoff the comparison should be done. |
A vector containing : sensibility, predictive positive value, and the F-score
Nicolas Jung, Frédéric Bertrand , Myriam Maumy-Bertrand.
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014). Cascade: a R-package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram, S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
data(Net)
data(Net_inf)
#Comparing true and inferred networks
F_score=NULL
#Here are the cutoff level tested
test.seq<-seq(0,max(abs(Net_inf@network*0.9)),length.out=200)
for(u in test.seq){
F_score<-rbind(F_score,Cascade::compare(Net,Net_inf,u))
}
matplot(test.seq,F_score,type="l",ylab="criterion value",xlab="cutoff level",lwd=2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.