graph.naivePruning {CTD}

Network pruning for disease-specific network determination

Description

Prune edges from a disease+control "differential" network that also occur in the control-only network.

Usage

graph.naivePruning(ig_dis, ig_ref)

Arguments

ig_dis

- The igraph object associated with the disease+reference trained differential network.

ig_ref

- The igraph object associated with the reference-only trained interaction network.

Value

ig_pruned - The pruned igraph object of the disease+reference differential network, with reference edges subtracted.

Examples

# Generate a 100 node "disease-control" network
adj_mat=matrix(0, nrow=100, ncol=100)
rows = sample(seq_len(100), 50, replace=TRUE)
cols = sample(seq_len(100), 50, replace=TRUE)
for (i in rows) {for (j in cols){adj_mat[i,j]=rnorm(1,0,1)}}
colnames(adj_mat)=sprintf("Metabolite%d", seq_len(100))
ig_dis = graph.adjacency(adj_mat, mode="undirected", weighted=TRUE)
# Generate a 100 node reference "control-only" network
adj_mat2=matrix(0, nrow=100, ncol=100)
rows2 = sample(seq_len(100), 50, replace=TRUE)
cols2 = sample(seq_len(100), 50, replace=TRUE)
for (i in rows2) {for (j in cols2){adj_mat2[i,j]=rnorm(1,0,1)}}
colnames(adj_mat2)=sprintf("Metabolite%d", seq_len(100))
ig_ref = graph.adjacency(adj_mat2, mode="undirected", weighted=TRUE)
ig_pruned=graph.naivePruning(ig_dis, ig_ref)