getBaseline: Get Baseline Vector.

In lpNet: Linear Programming Model for Network Inference

Description

The function returns a vector with the baseline values of each node in the network computed with the "doILP" function.

Usage

getBaseline(res, n, allpos=FALSE)

Arguments

res	Result returned by the "doILP" or "doILP_timeSeries" function.
n	Integer: the number of nodes of the inferred network.
allpos	Logical: should all variables be positive? Corresponds to learning only activating edges. Default: FALSE.

Value

Numeric matrix: the adjacency matrix of the network.

See Also

doILP

Examples

n <- 3 # number of genes
K <- 4 # number of experiments
T_ <- 4 # number of time points

# generate random observation matrix
obs <- array(rnorm(n*K*T_), c(n,K,T_))

baseline <- c(0.75, 0, 0)

delta <- rep(0.75, n)

# perturbation vector, entry is 0 if gene is inactivated and 1 otherwise
b <- c(0,1,1, # perturbation exp1: gene 1 perturbed, gene 2,3 unperturbed
       1,0,1, # perturbation exp2: gene 2 perturbed, gene 1,3 unperturbed
       1,1,0, # perturbation exp3....
       1,1,1)
            
delta_type <- "perGene"
lambda <- 1/10
annot <- getEdgeAnnot(n)

#infer the network
res <- doILP(obs, delta, lambda, b, n, K, T_, annot, delta_type, prior=NULL, sourceNode=NULL, 
             sinkNode=NULL, all.int=FALSE, all.pos=FALSE, flag_time_series=TRUE)

# make the adjacency matrix
adja <- getBaseline(res, n)

lpNet documentation built on Nov. 8, 2020, 7:08 p.m.