CGPFixSSMultiLayer: Module Identification for multi-layer network

In fairmiracle/AMOUNTAIN: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach

Description

Call C version of moduleIdentificationGPFixSSMultilayer

Usage

CGPFixSSMultiLayer(W, listzs, x0, a = 0.5, lambda = 1, maxiter = 50)

Arguments

W	edge score matrix of the network, n x n matrix
listzs	a list of node score vectors, each layer has a n-length vector
x0	initial solution, n-length vector
a	parameter in elastic net the same as in EuclideanProjectionENNORM
lambda	parameter in objective, coefficient of node score of other layers
maxiter	maximal interation of whole procedure

Value

a list containing solution for network 1 and network 2

Author(s)

Dong Li, [e-mail]

References

AMOUNTAIN

See Also

moduleIdentificationGPFixSSMultilayer

Examples

n = 100
k = 20
L = 5
theta = 0.5
cpl <- multilayernetworkSimulation(n,k,theta,L)
listz <- list()
for (i in 1:L){
listz[[i]] <- cpl[[i+2]]
}
moduleid <- cpl[[2]]
## use default parameters here
x <- CGPFixSSMultiLayer(cpl[[1]],listz,rep(1/n,n))
predictedid <- which(x[[2]]!=0)
recall <- length(intersect(predictedid,moduleid))/length(moduleid)
precise <- length(intersect(predictedid,moduleid))/length(predictedid)
Fscore <- (2*precise*recall/(precise+recall))

fairmiracle/AMOUNTAIN documentation built on May 16, 2019, 9:59 a.m.