make_commonlink {graphsim} | R Documentation |
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 |
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 tom.kelly@riken.jp
See Also
See also generate_expression
for computing the simulated data,
make_sigma
for computing the Sigma (\Sigma
) 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"))