Ac3net (version 1.2.2)

Ac3net.performance: Find the performance of the first network based on the second.

Description

Ac3net.performance takes two network list matrices. First is the predicted network and second is the reference true network. Returns performance scores.

Usage

Ac3net.performance(predictNet, referenceNet, data_, directed=TRUE)

Arguments

predictNet

The predicted network. A matrix that represents the links between two variables at each row. Namely, the element at row i and column 1 and the element at row i and column 2 are assumed to be paired or linked. There is no connection assumed among rows.

referenceNet

The reference network over which the performance will ve evaluated. A matrix that represents the links between two variables at each row. Namely, the element at row i and column 1 and the element at row i and column 2 are assumed to be paired or linked. There is no connection assumed among rows.

data_

The data matrix where the variables are available as row names of it. Only row names will be used to get all the variable names for the analaysis.

directed

It is TRUE by default, which means there is directionality assumed from column 1 to column 2 at each matrices. Therefore common links are the directionale common links. If it is FALSE, then the pairs assumed to be undirectional and all the common pairs are taken as common. E.g. If there is A--B in net1 and B--A in net2 then they are considered as common.

Value

Ac3net.performance returns an object with all the performance scores.

Details

Ac3net.performance takes two network list matrices and based on the first two columns it makes comparison betwee the predicted network and the reference network. If the argument directed is equal to TRUE, as in the default case, then it assumes directionally from column 1 to column 2 of the matrices and computes the performance based on it. E.g. For a pair of A-->B in the predicted network, if there is also A-->B in the reference network then it is taken as True Positive (TP) but the opposite direction B-->A is not accepted as TP. If the argument directed is equal to FALSE, then eitherA-->B or B-->A is acepted as TP, with no directional considerations.

References

G. Altay,"Directed Conservative Causal Core Gene Networks", bioRxiv, 2018. G. Altay, F. Emmert-Streib, "Inferring the conservative causal core of gene regulatory networks", BMC Systems Biology (2010) 4:132.

See Also

Ac3net.maxmim, Ac3net.cutoff,

Examples

# NOT RUN {
 # data(expdata)
 # data(truenet)
 # data(zeroindx) # zero indices for the example dataset, not in general.
 ## step by step to get Ac3net network
 # mim <- cor(t(expdata), method = "pearson")
 # mim[zeroindx, zeroindx] <- 0 #for the example dataset, not in general.
 # mim <- Ac3net.filtersames(mim=mim) #in case if there are same variables.
 #diag(mim) <-0
 #cutoff <- Ac3net.cutoff(mim, ratio_ = 0.002, PCmincutoff=0.6, PCmaxcutoff=0.96)
 #mim[abs(mim) < cutoff] <- 0
 #Ac3net.net <- Ac3net.maxmim(mim=mim, net_=TRUE, cutoff_= cutoff)
 ### one command could do the same as below if zeroindx was not available. ###
 ## ac3net.net <- Ac3net(DataOrMim=expdata, processed=FALSE, ratio_ = 0.002, PCmincutoff=0.6,
 #          PCmaxcutoff=0.96, cutoff=0, estmethod='pearson', pval=1, 
 #          iterations=10, MTC=FALSE, MTCmethod="BH" )
 
# performance <- Ac3net.performance(predictNet=ac3net.net, 
#                referenceNet = truenet, data_, directed=TRUE)
# performance
# }