Some basic criteria of comparison between actual and inferred network.

Allows comparison between actual and inferred network.

# S4 method for network,network,numeric
compare(Net, Net_inf, nv = 1)

Arguments

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.

Value

A vector containing : sensibility, predictive positive value, and the F-score

Methods

list("signature(Net = \"network\", Net_inf = \"network\", nv = \"numeric\")")

References

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.

Author

Nicolas Jung, Frédéric Bertrand , Myriam Maumy-Bertrand.

Examples

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)