make_distance: Generate Distance Matrix

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

Generate Distance Matrix

Description

Compute the distance matrix of using shortest paths of a (directed) igraph structure, normalising by the diameter of the network, preserving node/column/row names (and direction). This is used to compute the simulatted data for generate_expression (when dist = TRUE) by make_sigma_mat_dist_graph.

Usage

make_distance_graph(graph, directed = FALSE, absolute = FALSE)

make_distance_adjmat(mat, directed = FALSE, absolute = FALSE)

make_distance_comm(mat, directed = FALSE, absolute = FALSE)

make_distance_laplacian(mat, directed = FALSE, absolute = FALSE)

Arguments

graph	An igraph object. May be directed or weighted.
directed	logical. Whether directed information is passed to the distance matrix.
absolute	logical. Whether distances are scaled as the absolute difference from the diameter (maximum possible). Defaults to TRUE. The alternative is to calculate a relative difference from the diameter for a geometric decay in distance.
mat	precomputed adjacency or commonlink matrix.

Value

A numeric matrix of values in the range [0, 1] where higher values are closer in the network

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_state for resolving inhibiting states.

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

See also make_laplacian, make_commonlink, or make_adjmatrix for computing input matrices.

See also igraph for handling graph objects.

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

Other generate simulated expression functions: generate_expression(), make_sigma, make_state

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 relationships between nodes (geometrically decreasing by default)
distance_matrix_geom <- make_distance_adjmat(adjacency_matrix)
distance_matrix_geom

# compute nodes with relationships between nodes (arithmetically decreasing)
distance_matrix_abs <- make_distance_adjmat(adjacency_matrix, absolute = TRUE)
distance_matrix_abs

# compute Laplacian matrix
laplacian_matrix <- make_laplacian_graph(graph_test)
# compute distances from Laplacian
distance_matrix <- make_distance_laplacian(laplacian_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 relationships between nodes (geometrically decreasing by default)
graph_structure_distance_matrix_geom <- make_distance_adjmat(graph_structure_adjacency_matrix)
graph_structure_distance_matrix_geom
# visualise matrix
library("gplots")
heatmap.2(graph_structure_distance_matrix_geom, scale = "none", trace = "none",
          col = colorpanel(50, "white", "red"))
# compute nodes with relationships between nodes (arithmetically decreasing)
graph_structure_distance_matrix_abs <- make_distance_adjmat(graph_structure_adjacency_matrix,
                                                            absolute = TRUE)
graph_structure_distance_matrix_abs
# visualise matrix
library("gplots")
heatmap.2(graph_structure_distance_matrix_abs,
          scale = "none", trace = "none",
          col = colorpanel(50, "white", "red"))
          
# 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 relationships between nodes (geometrically decreasing by default)
TGFBeta_Smad_adjacency_matrix <- make_adjmatrix_graph(TGFBeta_Smad_graph)
TGFBeta_Smad_distance_matrix_geom <- make_distance_adjmat(TGFBeta_Smad_adjacency_matrix)
# visualise matrix
library("gplots")
heatmap.2(TGFBeta_Smad_distance_matrix_geom, scale = "none", trace = "none",
          col = colorpanel(50, "white", "red"))
# compute nodes with relationships between nodes (arithmetically decreasing)
TGFBeta_Smad_distance_matrix_abs <- make_distance_adjmat(TGFBeta_Smad_adjacency_matrix,
                        absolute = TRUE)
# visualise matrix
library("gplots")
heatmap.2(TGFBeta_Smad_distance_matrix_abs, scale = "none", trace = "none",
          col = colorpanel(50, "white", "red"))

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