graphsim (version 1.0.4)

make_state: Make State Matrix

Description

Functions to compute the matrix of states (1 for activating and -1 for inhibiting) for link signed correlations, from a vector of edge states to a signed adjacency matrix for use in generate_expression. This resolves edge states to determine the sign of all correlations between nodes in a network. These are computed interally for sigma matrices as required.

Usage

make_state_matrix(graph, state = NULL)

Value

An integer matrix indicating the resolved state (activating or inhibiting for each edge or path between nodes)

Arguments

graph

An igraph object. May be directed or weighted as long as a shortest path can be computed.

state

numeric vector. Vector of length E(graph). Sign used to calculate state matrix, may be an integer state or inferred directly from expected correlations for each edge. May be applied a scalar across all edges or as a vector for each edge respectively. May also be entered as text for "activating" or "inhibiting" or as integers for activating (0,1) or inhibiting (-1,2). Compatible with inputs for plot_directed. Vector input is supported either directly calling the function with a value for each edge in E(graph) or as an edge "attribute" in the igraph object (using E(g)$state <- states).

Author

Tom Kelly

See Also

See also generate_expression for computing the simulated data, make_sigma for computing the Sigma (\(\Sigma\)) matrix, and make_distance for computing distance from a graph object.

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_distance, make_laplacian, make_sigma, plot_directed()

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

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 state matrix for toy example
state_matrix <- make_state_matrix(graph_test)

# 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 state matrix for toy network
graph_structure_state_matrix <- make_state_matrix(graph_structure)
graph_structure_state_matrix

# compute state matrix for toy network with inhibitions
edge_state <- c(1, 1, -1, 1, 1, 1, 1, -1)
# edge states are a variable
graph_structure_state_matrix <- make_state_matrix(graph_structure, state = edge_state)
graph_structure_state_matrix

# compute state matrix for toy network with inhibitions
E(graph_structure)$state <- c(1, 1, -1, 1, 1, 1, 1, -1)
# edge states are a graph attribute
graph_structure_state_matrix <- make_state_matrix(graph_structure)
graph_structure_state_matrix

library("igraph")
graph_test_edges <- rbind(c("A", "B"), c("B", "C"), c("B", "D"))
graph_test <- graph.edgelist(graph_test_edges, directed = TRUE)
state_matrix <- make_state_matrix(graph_test)

# 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 sigma (\eqn{\Sigma}) matrix from geometric distance directly from TGF-\eqn{\Beta} pathway
TFGBeta_Smad_state <- E(TGFBeta_Smad_graph)$state
table(TFGBeta_Smad_state)
# states are edge attributes
state_matrix_TFGBeta_Smad <- make_state_matrix(TGFBeta_Smad_graph)
# visualise matrix
library("gplots")
heatmap.2(state_matrix_TFGBeta_Smad , scale = "none", trace = "none",
          dendrogram = "none", Rowv = FALSE, Colv = FALSE,
          col = colorpanel(50, "blue", "white", "red"))

# compare the states to the sign of expected correlations in the sigma matrix
sigma_matrix_TFGBeta_Smad_inhib <- make_sigma_mat_dist_graph(TGFBeta_Smad_graph,
                                                             cor = 0.8,
                                                             absolute = FALSE)
# visualise matrix
heatmap.2(sigma_matrix_TFGBeta_Smad_inhib,
          scale = "none", trace = "none",
          dendrogram = "none", Rowv = FALSE, Colv = FALSE,
          col = colorpanel(50, "blue", "white", "red"))

# compare the states to the sign of final correlations in the simulated matrix
TFGBeta_Smad_data <- generate_expression(100, TGFBeta_Smad_graph, cor = 0.8)
heatmap.2(cor(t(TFGBeta_Smad_data)), scale = "none", trace = "none",
          dendrogram = "none", Rowv = FALSE, Colv = FALSE,
          col = colorpanel(50, "blue", "white", "red"))