make_commonlink: Generate Common Link Matrix

In TomKellyGenetics/graphsim: Simulate Expression Data from 'igraph' Networks

Generate Common Link Matrix

Description

Compute the common link matrix of a (directed) igraph structure, preserving node / column / row names (and direction). We can compute the common links between each pair of nodes. This shows how many nodes are mutually connected to both of the nodes in the matrix (how many paths of length 2 exist between them).

Usage

make_commonlink_adjmat(adj_mat)

make_commonlink_graph(graph, directed = FALSE)

Arguments

adj_mat	precomputed adjacency matrix.
graph	An igraph object. May be directed or weighted.
directed	logical. Whether directed information is passed to the adjacency matrix.

Value

An integer matrix of number of links shared between nodes

Author(s)

Tom Kelly [e-mail]

See Also

See also generate_expression for computing the simulated data, make_sigma for computing the Sigma (Σ) matrix, make_distance for computing distance from a graph object, make_state for resolving inhibiting states.

See also plot_directed for plotting graphs or heatmap.2 for plotting matrices.

See also make_laplacian or make_adjmatrix for computing input matrices.

See also igraph for handling graph objects.

Other graphsim functions: generate_expression(), make_adjmatrix, make_distance, make_laplacian, make_sigma, make_state, plot_directed()

Other graph conversion functions: make_adjmatrix, make_laplacian

Examples

# construct a synthetic graph module
library("igraph")
graph_test_edges <- rbind(c("A", "B"), c("B", "C"), c("B", "D"))
graph_test <- graph.edgelist(graph_test_edges, directed = TRUE)

# compute adjacency matrix for toy example
adjacency_matrix <- make_adjmatrix_graph(graph_test)
# compute nodes with shared edges to a 3rd node
common_link_matrix <- make_commonlink_adjmat(adjacency_matrix)
common_link_matrix

# construct a synthetic graph network
graph_structure_edges <- rbind(c("A", "C"), c("B", "C"), c("C", "D"), c("D", "E"),
                               c("D", "F"), c("F", "G"), c("F", "I"), c("H", "I"))
graph_structure <- graph.edgelist(graph_structure_edges, directed = TRUE)
# compute adjacency matrix for toy network
graph_structure_adjacency_matrix <- make_adjmatrix_graph(graph_structure)
# compute nodes with shared edges to a 3rd node
graph_structure_common_link_matrix <- make_commonlink_adjmat(graph_structure_adjacency_matrix)
graph_structure_common_link_matrix

# import graph from package for reactome pathway
# TGF-\eqn{\Beta} receptor signaling activates SMADs (R-HSA-2173789)
TGFBeta_Smad_graph <- identity(TGFBeta_Smad_graph)
# compute nodes with shared edges to a 3rd node 
TGFBeta_Smad_adjacency_matrix <- make_adjmatrix_graph(TGFBeta_Smad_graph)
TGFBeta_Smad_common_link_matrix <- make_commonlink_adjmat(TGFBeta_Smad_adjacency_matrix)
# we show summary statistics as the graph is large
dim(TGFBeta_Smad_common_link_matrix)
TGFBeta_Smad_common_link_matrix[1:12, 1:12]
# visualise matrix
library("gplots")
heatmap.2(TGFBeta_Smad_common_link_matrix, scale = "none", trace = "none",
          col = colorpanel(50, "white", "red"))

TomKellyGenetics/graphsim documentation built on Sept. 17, 2022, 1:37 a.m.