generate_expression: Generate Simulated Expression
In TomKellyGenetics/graphsim: Simulate Expression Data from 'igraph' Networks
generate_expression R Documentation
Generate Simulated Expression
Description
Compute simulated continuous expression data from a graph
network structure. Requires an igraph pathway
structure and a 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.
Uses graph structure to pass a sigma covariance matrix from
make_sigma_mat_graph or
make_sigma_mat_dist_graph on to
rmvnorm. By default data is generated with a mean of
0 and standard deviation of 1 for each gene (with correlations between
derived from the graph structure).
Usage
generate_expression(
n,
graph,
state = NULL,
cor = 0.8,
mean = 0,
sd = 1,
comm = FALSE,
dist = FALSE,
absolute = FALSE,
laplacian = FALSE
)
generate_expression_mat(
n,
mat,
state = NULL,
cor = 0.8,
mean = 0,
sd = 1,
comm = FALSE,
dist = FALSE,
absolute = FALSE,
laplacian = FALSE
)
Arguments
n
number of observations (simulated samples).
graph
An igraph object. May must be
directed if states are used.
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.
Also takes a pre-computed state matrix from make_state
if applied to the same graph multiple times.
cor
numeric. Simulated maximum correlation/covariance of two
adjacent nodes. Default to 0.8.
mean
mean value of each simulated gene. Defaults to 0.
May be entered as a scalar applying to
all genes or a vector with a separate value for each.
sd
standard deviations of each gene. Defaults to 1.
May be entered as a scalar applying to
all genes or a vector with a separate value for each.
comm, absolute, laplacian
logical. Parameters for Sigma matrix
generation. Passed on to make_sigma
or make_sigma.
dist
logical. Whether a graph distance
make_sigma_mat_graph or derived matrix
make_sigma_mat_dist_graph is used to compute the
sigma matrix (using make_distance).
mat
precomputed adjacency, laplacian, commonlink, or scaled
distance matrix (generated by make_distance).
Value
numeric matrix of simulated data (log-normalised counts)
Author(s)
Tom Kelly tom.kelly@riken.jp
See Also
See also make_sigma for computing the Sigma (Σ) matrix,
make_distance for computing distance from a graph object,
and
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:
make_adjmatrix,
make_commonlink,
make_distance,
make_laplacian,
make_sigma,
make_state,
plot_directed()
Other generate simulated expression functions:
make_distance,
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 a simulated dataset for toy example
# n = 100 samples
# cor = 0.8 max correlation between samples
# absolute = FALSE (geometric distance by default)
test_data <- generate_expression(100, graph_test, cor = 0.8)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(test_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(test_data)), scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(graph_test, cor = 0.8)
heatmap.2(make_sigma_mat_graph(graph_test, cor = 0.8),
scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# compute adjacency matrix for toy example
adjacency_matrix <- make_adjmatrix_graph(graph_test)
# generate simulated data from adjacency matrix input
test_data <- generate_expression_mat(100, adjacency_matrix, cor = 0.8)
# compute a simulated dataset for toy example
# n = 100 samples
# cor = 0.8 max correlation between samples
# absolute = TRUE (arithmetic distance)
test_data <- generate_expression(100, graph_test, cor = 0.8, absolute = TRUE)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(test_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(test_data)),
scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(graph_test, cor = 0.8)
heatmap.2(make_sigma_mat_graph(graph_test, cor = 0.8),
scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# 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 a simulated dataset for toy network
# n = 250 samples
# state = edge_state (properties of each edge)
# cor = 0.95 max correlation between samples
# absolute = FALSE (geometric distance by default)
edge_state <- c(1, 1, -1, 1, 1, 1, 1, -1)
structure_data <- generate_expression(250, graph_structure,
state = edge_state, cor = 0.95)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(structure_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(structure_data)), scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(graph_structure,
state = edge_state, cor = 0.8)
heatmap.2(make_sigma_mat_graph(graph_structure,
state = edge_state, cor = 0.8),
scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# compute adjacency matrix for toy network
graph_structure_adjacency_matrix <- make_adjmatrix_graph(graph_structure)
# define states for for each edge
edge_state <- c(1, 1, -1, 1, 1, 1, 1, -1)
# generate simulated data from adjacency matrix input
structure_data <- generate_expression_mat(250, graph_structure_adjacency_matrix,
state = edge_state, cor = 0.8)
# compute a simulated dataset for toy network
# n = 1000 samples
# state = TGFBeta_Smad_state (properties of each edge)
# cor = 0.75 max correlation between samples
# absolute = FALSE (geometric distance by default)
# compute states directly from graph attributes for TGF-\eqn{\Beta} pathway
TGFBeta_Smad_state <- E(TGFBeta_Smad_graph)$state
table(TGFBeta_Smad_state)
# generate simulated data
TGFBeta_Smad_data <- generate_expression(1000, TGFBeta_Smad_graph, cor = 0.75)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(TGFBeta_Smad_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(TGFBeta_Smad_data)), scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_dist_graph(TGFBeta_Smad_graph, cor = 0.75)
heatmap.2(make_sigma_mat_dist_graph(TGFBeta_Smad_graph, cor = 0.75),
scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
# generate simulated data (absolute distance and shared edges)
TGFBeta_Smad_data <- generate_expression(1000, TGFBeta_Smad_graph,
cor = 0.75, absolute = TRUE, comm = TRUE)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(TGFBeta_Smad_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(TGFBeta_Smad_data)), scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(TGFBeta_Smad_graph,
cor = 0.75, comm = TRUE)
heatmap.2(make_sigma_mat_graph(TGFBeta_Smad_graph, cor = 0.75, comm = TRUE),
scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
TomKellyGenetics/graphsim documentation built on Sept. 17, 2022, 1:37 a.m.
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| generate_expression | R Documentation |
Generate Simulated Expression
Description
Compute simulated continuous expression data from a graph
network structure. Requires an igraph pathway
structure and a 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.
Uses graph structure to pass a sigma covariance matrix from
make_sigma_mat_graph or
make_sigma_mat_dist_graph on to
rmvnorm. By default data is generated with a mean of
0 and standard deviation of 1 for each gene (with correlations between
derived from the graph structure).
Usage
generate_expression( n, graph, state = NULL, cor = 0.8, mean = 0, sd = 1, comm = FALSE, dist = FALSE, absolute = FALSE, laplacian = FALSE ) generate_expression_mat( n, mat, state = NULL, cor = 0.8, mean = 0, sd = 1, comm = FALSE, dist = FALSE, absolute = FALSE, laplacian = FALSE )
Arguments
n |
number of observations (simulated samples). |
graph |
An |
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 |
cor |
numeric. Simulated maximum correlation/covariance of two adjacent nodes. Default to 0.8. |
mean |
mean value of each simulated gene. Defaults to 0. May be entered as a scalar applying to all genes or a vector with a separate value for each. |
sd |
standard deviations of each gene. Defaults to 1. May be entered as a scalar applying to all genes or a vector with a separate value for each. |
comm, absolute, laplacian |
logical. Parameters for Sigma matrix
generation. Passed on to |
dist |
logical. Whether a graph distance
|
mat |
precomputed adjacency, laplacian, commonlink, or scaled
distance matrix (generated by |
Value
numeric matrix of simulated data (log-normalised counts)
Author(s)
Tom Kelly tom.kelly@riken.jp
See Also
See also make_sigma for computing the Sigma (Σ) matrix,
make_distance for computing distance from a graph object,
and
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:
make_adjmatrix,
make_commonlink,
make_distance,
make_laplacian,
make_sigma,
make_state,
plot_directed()
Other generate simulated expression functions:
make_distance,
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 a simulated dataset for toy example
# n = 100 samples
# cor = 0.8 max correlation between samples
# absolute = FALSE (geometric distance by default)
test_data <- generate_expression(100, graph_test, cor = 0.8)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(test_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(test_data)), scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(graph_test, cor = 0.8)
heatmap.2(make_sigma_mat_graph(graph_test, cor = 0.8),
scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# compute adjacency matrix for toy example
adjacency_matrix <- make_adjmatrix_graph(graph_test)
# generate simulated data from adjacency matrix input
test_data <- generate_expression_mat(100, adjacency_matrix, cor = 0.8)
# compute a simulated dataset for toy example
# n = 100 samples
# cor = 0.8 max correlation between samples
# absolute = TRUE (arithmetic distance)
test_data <- generate_expression(100, graph_test, cor = 0.8, absolute = TRUE)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(test_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(test_data)),
scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(graph_test, cor = 0.8)
heatmap.2(make_sigma_mat_graph(graph_test, cor = 0.8),
scale = "none", trace = "none",
col = colorpanel(50, "white", "red"))
# 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 a simulated dataset for toy network
# n = 250 samples
# state = edge_state (properties of each edge)
# cor = 0.95 max correlation between samples
# absolute = FALSE (geometric distance by default)
edge_state <- c(1, 1, -1, 1, 1, 1, 1, -1)
structure_data <- generate_expression(250, graph_structure,
state = edge_state, cor = 0.95)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(structure_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(structure_data)), scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(graph_structure,
state = edge_state, cor = 0.8)
heatmap.2(make_sigma_mat_graph(graph_structure,
state = edge_state, cor = 0.8),
scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# compute adjacency matrix for toy network
graph_structure_adjacency_matrix <- make_adjmatrix_graph(graph_structure)
# define states for for each edge
edge_state <- c(1, 1, -1, 1, 1, 1, 1, -1)
# generate simulated data from adjacency matrix input
structure_data <- generate_expression_mat(250, graph_structure_adjacency_matrix,
state = edge_state, cor = 0.8)
# compute a simulated dataset for toy network
# n = 1000 samples
# state = TGFBeta_Smad_state (properties of each edge)
# cor = 0.75 max correlation between samples
# absolute = FALSE (geometric distance by default)
# compute states directly from graph attributes for TGF-\eqn{\Beta} pathway
TGFBeta_Smad_state <- E(TGFBeta_Smad_graph)$state
table(TGFBeta_Smad_state)
# generate simulated data
TGFBeta_Smad_data <- generate_expression(1000, TGFBeta_Smad_graph, cor = 0.75)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(TGFBeta_Smad_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(TGFBeta_Smad_data)), scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_dist_graph(TGFBeta_Smad_graph, cor = 0.75)
heatmap.2(make_sigma_mat_dist_graph(TGFBeta_Smad_graph, cor = 0.75),
scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
# generate simulated data (absolute distance and shared edges)
TGFBeta_Smad_data <- generate_expression(1000, TGFBeta_Smad_graph,
cor = 0.75, absolute = TRUE, comm = TRUE)
##' # visualise matrix
library("gplots")
# expression data
heatmap.2(TGFBeta_Smad_data, scale = "none", trace = "none",
col = colorpanel(50, "blue", "white", "red"))
# correlations
heatmap.2(cor(t(TGFBeta_Smad_data)), scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
# expected correlations (\eqn{\Sigma})
sigma_matrix <- make_sigma_mat_graph(TGFBeta_Smad_graph,
cor = 0.75, comm = TRUE)
heatmap.2(make_sigma_mat_graph(TGFBeta_Smad_graph, cor = 0.75, comm = TRUE),
scale = "none", trace = "none",
dendrogram = "none", Rowv = FALSE, Colv = FALSE,
col = colorpanel(50, "blue", "white", "red"))
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