calcActivation: Calculate Activation Matrix

In lpNet: Linear Programming Model for Network Inference

Description

Calculate the activation matrix assuming that the signaling is deterministically propagated along the network. For a given network and perturbation experiment the theoretical states of the genes are computed. So, if a gene has been silenced in an experiment, then the state of this gene is assumed to be inactive, otherwise if its inflow (coming from parent nodes) is activating, it is active. Cycles within a network are not resolved, therefore this function can be used only for networks without cycles. This function is also used to generate the network states for time-series data (by generateTimeSeriesNetStates), in which case flag_gen_data is set to true, and the activation matrix is calculated without taking the edges sign into account.

Usage

calcActivation(T_nw, b, n, K, flag_gen_data = FALSE)

Arguments

T_nw	Adjacency matrix: the network which is used to compute the activities and inactivities.
b	Vector of 0/1 values describing the experiments (entry is 0 if gene is inactivated in the respetive experiment and 1 otherwise). The measurements of the genes of each experiment are appended as a long vector.
n	Integer: number of genes.
K	Integer: number of perturbation experiments.
flag_gen_data	Logical: if set to TRUE the edges sign will not be taken into account. It should be TRUE if the function is used to generate the network states for time-series data.

Value

Matrix of 0/1 values; rows corresponding to genes, columns to experiments. If an entry is 1, it means that the corresponding gene is active in the corresponing experiment and inactive otherwise.

Examples

n <- 5 # number of genes
K <- 7 # number of perturbations experiments

# perturbation vector, entry is 0 if gene is inactivated and 1 otherwise
b <- c(0,1,1,1,1, # perturbation exp1: gene 1 perturbed, gene 2-5 unperturbed
       1,0,1,1,1, # perturbation exp2: gene 2 perturbed, gene 1,3,4,5 unperturbed
       1,1,0,1,1, # perturbation exp3....
       1,1,1,0,1,
       1,1,1,1,0,
       1,0,0,1,1,
       1,1,1,1,1)
       
# example network
T_nw <- matrix(c(0,1,1,0,0,
                 0,0,0,-1,0,
                 0,0,0,1,0,
                 0,0,0,0,1,
                 0,0,0,0,0), nrow=n,ncol=n,byrow=TRUE)
                 
# compute theoretical activation of genes from example network with given perturbations
act_mat <- calcActivation(T_nw, b, n, K)

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