| Title: | Generate RNA-Seq Data from Gene-Gene Association Networks |
|---|---|
| Description: | Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) <doi:10.18637/jss.v098.i12>. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks. |
| Authors: | Tyler Grimes [aut, cre], Somnath Datta [aut] |
| Maintainer: | Tyler Grimes <[email protected]> |
| License: | GPL-2 | GPL-3 |
| Version: | 1.1.3 |
| Built: | 2025-03-13 05:18:15 UTC |
| Source: | https://github.com/cran/SeqNet |
Internal function for adding a set of modules to the network
add_modules_to_network(network, module_list)add_modules_to_network(network, module_list)
network |
The network to modify. |
module_list |
A list of 'network_module' objects to add to the network. |
The modified network.
Adds a random module of a given size to the network
add_random_module_to_network(network, module_size, ...)add_random_module_to_network(network, module_size, ...)
network |
The 'network' object to modify. |
module_size |
The size of the module to generate. |
... |
Additional arguments passed into random_module(). |
The modified 'network' object.
# This function provides an alternative way to iteratively add random # modules to the network. It uses a weighted sampling of nodes, where # nodes that haven't been selected for a module have a higher probability # of being sampled for the new module. nw <- create_empty_network(100) plot(nw) # An empty network of 100 nodes. # Add random modules of size 10 to the network, 1 at a time. # By plotting the network each time, we can watch it grow. set.seed(12345) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) # Etc.# This function provides an alternative way to iteratively add random # modules to the network. It uses a weighted sampling of nodes, where # nodes that haven't been selected for a module have a higher probability # of being sampled for the new module. nw <- create_empty_network(100) plot(nw) # An empty network of 100 nodes. # Add random modules of size 10 to the network, 1 at a time. # By plotting the network each time, we can watch it grow. set.seed(12345) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) plot(nw <<- add_random_module_to_network(nw, 10)) # Etc.
This modification can be used if it is desired to simulate from a single GGM rather than averaging over the GGMs for each module.
as_single_module(network)as_single_module(network)
network |
The 'network' object to modify |
The modified 'network' object.
# This function can be used prior to generating weights for the network # connections. With multiple modules in the network, the weighted network may # gain conditional dependencies between nodes across modules. If the network # is reduced to a single module prior to generating weights, then the # weighted and unweighted networks will maintain the same structure. nw <- random_network(20, n_modules = 3) g <- plot(nw) nw <- gen_partial_correlations(nw) plot(nw, g) # Additional edges appear from conditional dependencies across modules. nw <- remove_weights(nw) # Remove weights to avoid warning message in next call. nw <- as_single_module(nw) nw <- gen_partial_correlations(nw) plot(nw, g) # With only one module, the weighted network has the same structure.# This function can be used prior to generating weights for the network # connections. With multiple modules in the network, the weighted network may # gain conditional dependencies between nodes across modules. If the network # is reduced to a single module prior to generating weights, then the # weighted and unweighted networks will maintain the same structure. nw <- random_network(20, n_modules = 3) g <- plot(nw) nw <- gen_partial_correlations(nw) plot(nw, g) # Additional edges appear from conditional dependencies across modules. nw <- remove_weights(nw) # Remove weights to avoid warning message in next call. nw <- as_single_module(nw) nw <- gen_partial_correlations(nw) plot(nw, g) # With only one module, the weighted network has the same structure.
C++ implementation to check if a matrix is an adjacency matrix
check_adjacency_cpp(m)check_adjacency_cpp(m)
m |
A matrix to check. |
Returns 0 if the matrix is an adjacency matrix. If the matrix is not square, returns 1; if the diagonal entries are not all zero, returns 2; if the matrix is not symmetric, returns 3; if the matrix contains values other than 0 or 1, returns 4.
C++ implementation to obtain connected components in a graph.
components_in_adjacency(adj)components_in_adjacency(adj)
adj |
An adjacency matrix. |
Returns a matrix with 2 columns containing the indicies in the lower-triangle of the matrix that are nonzero.
Connect disconnected components in an adjacency matrix
connect_module_structure( adj, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5 )connect_module_structure( adj, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5 )
adj |
An adjacency matrix to modify. |
weights |
(Optional) weights used for sampling nodes. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
A modified adjacency matrix
This function is used in random_module_structure to
reconnect any disconnected components after edge removal and rewiring.
When connecting two components, a node is sampled from each component
with probability that is dependent on node degree; those two nodes are then
connected, which connects the components.
# This function is used in `random_module_structure()` to reconnect any # disconnected components. To demonstrate, we'll create a random structure, # remove connections to one of the nodes (that node will then be a disconnected # component), and use `connect_module_structure()` to reconnect it back to # the main component. adj <- random_module_structure(10) adj <- remove_connections_to_node(adj, 1, prob_remove = 1) # Note that there are now two components in the network: components_in_adjacency(adj) g <- plot_network(adj) # After connecting, the network contains one component. adj <- connect_module_structure(adj) components_in_adjacency(adj) plot_network(adj, g)# This function is used in `random_module_structure()` to reconnect any # disconnected components. To demonstrate, we'll create a random structure, # remove connections to one of the nodes (that node will then be a disconnected # component), and use `connect_module_structure()` to reconnect it back to # the main component. adj <- random_module_structure(10) adj <- remove_connections_to_node(adj, 1, prob_remove = 1) # Note that there are now two components in the network: components_in_adjacency(adj) g <- plot_network(adj) # After connecting, the network contains one component. adj <- connect_module_structure(adj) components_in_adjacency(adj) plot_network(adj, g)
The returned data frame can be saved as a .csv file. Then, in Cytoscape use File -> Import -> Network -> File. Select the .csv file containing the data frame generated by this function. There will be a pop-up window. The source, interaction, and target columns should automatically be identified. Click OK.
create_cytoscape_file(g)create_cytoscape_file(g)
g |
A 'network_plot' object. See |
A data frame containing an edge table that can be saved as a .csv file to be used in Cytoscape.
nw <- random_network(10) g <- plot(nw) nw_plot_cytoscape <- create_cytoscape_file(g) # Save the edge table in a .csv file to be used in cytoscape. write.table(nw_plot_cytoscape, file.path(tempdir(), "file_name.csv"), sep = ",", row.names = FALSE, col.names = TRUE, quote = FALSE)nw <- random_network(10) g <- plot(nw) nw_plot_cytoscape <- create_cytoscape_file(g) # Save the edge table in a .csv file to be used in cytoscape. write.table(nw_plot_cytoscape, file.path(tempdir(), "file_name.csv"), sep = ",", row.names = FALSE, col.names = TRUE, quote = FALSE)
Create a module
create_empty_module(nodes)create_empty_module(nodes)
nodes |
A numeric vector indicating which nodes in the network are contained in this module. |
A 'network_module' object.
module <- create_empty_module(1:10) plot(module) # A module with no edges.module <- create_empty_module(1:10) plot(module) # A module with no edges.
Creates a 'network' object containing no modules.
create_empty_network(p)create_empty_network(p)
p |
The number of nodes in the network |
A network object.
nw <- create_empty_network(10) plot(nw) # A network with no edges.nw <- create_empty_network(10) plot(nw) # A network with no edges.
The edges in the module will be set to the edges in the adjacency matrix.
The edges are undirected, and only the lower triangle of the
matrix is considered. See set_module_edges for more details.
create_module_from_adjacency_matrix( adjacency_matrix, nodes = NULL, module_name = NULL, run_checks = TRUE )create_module_from_adjacency_matrix( adjacency_matrix, nodes = NULL, module_name = NULL, run_checks = TRUE )
adjacency_matrix |
The adjacency matrix used to create the module. |
nodes |
A numeric vector indicating which nodes in the network are contained in this module. |
module_name |
(optional) Character string specifying the name of the
module. If |
run_checks |
If |
A 'network_module' object.
nw <- random_network(10) nw <- gen_partial_correlations(nw) adj_mat <- get_adjacency_matrix(nw) create_module_from_adjacency_matrix(adj_mat)nw <- random_network(10) nw <- gen_partial_correlations(nw) adj_mat <- get_adjacency_matrix(nw) create_module_from_adjacency_matrix(adj_mat)
The edge weights in the module will be set to the corresponding values
in the association matrix. The edges are undirected, and only the lower
triangle of the matrix is considered. See set_module_weights
for more details.
create_module_from_association_matrix( association_matrix, nodes = NULL, module_name = NULL )create_module_from_association_matrix( association_matrix, nodes = NULL, module_name = NULL )
association_matrix |
The association matrix used to create the module. |
nodes |
A numeric vector indicating which nodes in the network are contained in this module. |
module_name |
(optional) Character string specifying the name of the
module. If |
A 'network_module' object.
nw <- random_network(10) nw <- gen_partial_correlations(nw) assoc_mat <- get_association_matrix(nw) create_module_from_association_matrix(assoc_mat)nw <- random_network(10) nw <- gen_partial_correlations(nw) assoc_mat <- get_association_matrix(nw) create_module_from_association_matrix(assoc_mat)
Creates a collection of modules containing randomly samples genes.
create_modules_for_network( n_modules, p, avg_module_size = 50, sd_module_size = 50, min_module_size = 10, max_module_size = 200, sample_link_nodes_fn = sample_link_nodes, sample_module_nodes_fn = sample_module_nodes, ... )create_modules_for_network( n_modules, p, avg_module_size = 50, sd_module_size = 50, min_module_size = 10, max_module_size = 200, sample_link_nodes_fn = sample_link_nodes, sample_module_nodes_fn = sample_module_nodes, ... )
n_modules |
The number of modules to include in the network. |
p |
The number of nodes in the network. |
avg_module_size |
The average number of nodes in a module. |
sd_module_size |
The standard deviation of module size. |
min_module_size |
The minimum number of nodes in a module. |
max_module_size |
A positive value. Any generated module sizes above this value will be reduced to 'max_module_size'. Set to 'Inf' to avoid this truncation. |
sample_link_nodes_fn |
A function used for sampling link nodes for a new module. |
sample_module_nodes_fn |
A function used for sampling nodes for a new module. |
... |
Additional arguments passed to |
A list containing the indices for genes contained in each module.
Grimes T, Datta S (2021). “SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.” Journal of Statistical Software, 98(12), 1–49. doi:10.18637/jss.v098.i12.
# Create a two modules (having random structures and sizes) from a pool # of 100 nodes. create_modules_for_network(n_modules = 2, p = 100) # Set n_modules = NULL to continue making modules until all nodes have # been selected at least once. create_modules_for_network(n_modules = NULL, p = 100)# Create a two modules (having random structures and sizes) from a pool # of 100 nodes. create_modules_for_network(n_modules = 2, p = 100) # Set n_modules = NULL to continue making modules until all nodes have # been selected at least once. create_modules_for_network(n_modules = NULL, p = 100)
Create a network object from an adjacency matrix
create_network_from_adjacency_matrix(adjacency_matrix, ...)create_network_from_adjacency_matrix(adjacency_matrix, ...)
adjacency_matrix |
The adjacency matrix for the network. Since the adjacency matrix only provides information on the global connections, the resulting 'network' object will consist of a single module containing these connections. |
... |
Additional arguments passed to
|
A network object.
adj_mat <- random_module_structure(10) nw <- create_network_from_adjacency_matrix(adj_mat) all(adj_mat == get_adjacency_matrix(nw))adj_mat <- random_module_structure(10) nw <- create_network_from_adjacency_matrix(adj_mat) all(adj_mat == get_adjacency_matrix(nw))
Create a network object from an association matrix
create_network_from_association_matrix(association_matrix, ...)create_network_from_association_matrix(association_matrix, ...)
association_matrix |
The association matrix for the network. Since the association matrix only provides information on the global connections, the resulting 'network' object will consist of a single weighted module containing these connections. The edge weights, i.e. the partial correlations, will correspond to the nonzero values in the matrix. |
... |
Additional arguments passed to
|
A network object.
# Create a random weighted network and extract the association matrix from it. nw <- random_network(10) nw <- gen_partial_correlations(nw) assoc_mat <- get_association_matrix(nw) # Any association matrix can be used to directly create a network object. # However, the created network will only contain one module. nw_from_assoc <- create_network_from_association_matrix(assoc_mat) all(get_adjacency_matrix(nw) == get_adjacency_matrix(nw_from_assoc))# Create a random weighted network and extract the association matrix from it. nw <- random_network(10) nw <- gen_partial_correlations(nw) assoc_mat <- get_association_matrix(nw) # Any association matrix can be used to directly create a network object. # However, the created network will only contain one module. nw_from_assoc <- create_network_from_association_matrix(assoc_mat) all(get_adjacency_matrix(nw) == get_adjacency_matrix(nw_from_assoc))
Generates a 'network' object from a list of 'network_modules', The modules
are assumed to have their local network structure already generated.
Individual modules can be generated using the random_module
function.
create_network_from_modules( p, module_list, node_names = as.character(1:p), ... )create_network_from_modules( p, module_list, node_names = as.character(1:p), ... )
p |
The number of nodes in the graph |
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module_list |
A named list of 'network_module' objects. |
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node_names |
(optional) Vector of strings providing names for each node in the graph. Default names are "1", "2", ..., "p". |
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... |
Arguments to be passed to other methods. Possible arguments include:
|
A network object.
# Networks can be crafted manually by first constructing the individual # modules, then putting them together to create a network. module_1 <- random_module(1:10) # A module containing nodes 1-10 module_2 <- random_module(5:15) # A module containing nodes 5-15 # Create a network containing 20 nodes and the two modules. nw <- create_network_from_modules(20, list(module_1, module_2)) nw # Note: nodes 16-20 are not in a module, so they have no connections. plot(nw)# Networks can be crafted manually by first constructing the individual # modules, then putting them together to create a network. module_1 <- random_module(1:10) # A module containing nodes 1-10 module_2 <- random_module(5:15) # A module containing nodes 5-15 # Create a network containing 20 nodes and the two modules. nw <- create_network_from_modules(20, list(module_1, module_2)) nw # Note: nodes 16-20 are not in a module, so they have no connections. plot(nw)
The Zero-Inflated Negative Binomial Distribution
dzinb(x, size, mu, rho = 0, log = FALSE)dzinb(x, size, mu, rho = 0, log = FALSE)
x |
A vector of quantities. |
size |
The dispersion paramater used in |
mu |
The mean parameter used in |
rho |
The zero-inflation parameter. |
log |
Logical; if |
The value(s) of the density function evaluated at x.
Grimes T, Datta S (2021). “SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.” Journal of Statistical Software, 98(12), 1–49. doi:10.18637/jss.v098.i12.
x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))
Constructs the empirical CDF, F, for a set of observations, x, and returns F(x).
ecdf_cpp(x)ecdf_cpp(x)
x |
The observation to construct the empirical CDF from. |
Returns the values for F(x).
C++ implementation for obtaining an edge list from adjacency matrix
edges_from_adjacency_cpp(adj)edges_from_adjacency_cpp(adj)
adj |
An adjacency matrix. |
Returns a matrix with 2 columns containing the indicies in the lower-triangle of the matrix that are nonzero.
The observations in the reference dataset should be as homogeneous as possible. For example, we should not expect differential expression or differential connectivity of genes within the sample. If the data are heterogeneous, the estimation of the parameters may be unreliable.
est_params_from_reference(reference, verbose = TRUE)est_params_from_reference(reference, verbose = TRUE)
reference |
Either a vector or data.frame of counts from a reference gene expression profile. If a data.frame is provided, each column should correspond to a gene. |
verbose |
Boolean indicator for message output. |
Returns a list containing a matrix of parameter estimates 'size',
'mu', and 'rho' for each gene in the reference, and the reference dataset
used. The parameter matrix can be used in gen_zinb.
# The internal reference dataset already contains ZINB parameter estimates, # so running est_params_from_reference() is not necessary. To simulate # ZINB data from a different RNA-seq reference dataset, the data can # be passed into gen_zinb() directly using the 'reference' argument, and # est_params_from_reference() will be used automatically (i.e. the user # does not need to call this function directly). # An example using the reference dataset data(reference) # The RNA-seq dataset should have samples as rows and genes as columns: rnaseq <- reference$rnaseq # Estimate ZINB params for first ten genes. params <- est_params_from_reference(rnaseq[, 1:10])$params # However, the previous call is not needed for simulated ZINB data. # The RNA-seq dataset can be passed directly to `gen_zinb()`. nw <- random_network(10) x <- gen_zinb(20, nw, reference = rnaseq[, 1:10])$x # Pass in 'rnaseq' directly.# The internal reference dataset already contains ZINB parameter estimates, # so running est_params_from_reference() is not necessary. To simulate # ZINB data from a different RNA-seq reference dataset, the data can # be passed into gen_zinb() directly using the 'reference' argument, and # est_params_from_reference() will be used automatically (i.e. the user # does not need to call this function directly). # An example using the reference dataset data(reference) # The RNA-seq dataset should have samples as rows and genes as columns: rnaseq <- reference$rnaseq # Estimate ZINB params for first ten genes. params <- est_params_from_reference(rnaseq[, 1:10])$params # However, the previous call is not needed for simulated ZINB data. # The RNA-seq dataset can be passed directly to `gen_zinb()`. nw <- random_network(10) x <- gen_zinb(20, nw, reference = rnaseq[, 1:10])$x # Pass in 'rnaseq' directly.
Generates data based on the multivariate normal distribution parameterized by a zero mean vector and a covariance matrix. Observations are generated for each module in the network individually, and the covariance matrix is set to the inverse of the standardized association matrix for the module. Observations are combined for gene i by taking the sum across the m_i modules containing it and dividing by sqrt(m_i).
gen_gaussian(n, ...)gen_gaussian(n, ...)
n |
The number of samples to generate. If multiple networks are provided, n samples are generated per network. |
... |
The 'network' object(s) to generate data from. Can be a single network, many networks, or a single list of networks. |
A list containing the n by p matrix of samples and the 'network' object used to generate them.
Grimes T, Datta S (2021). “SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.” Journal of Statistical Software, 98(12), 1–49. doi:10.18637/jss.v098.i12.
nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network. x <- gen_gaussian(20, nw)$x # Simulate 20 Gaussian observations from network.nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network. x <- gen_gaussian(20, nw)$x # Simulate 20 Gaussian observations from network.
Random partial correlations are generated to weigh the network connections. If multiple networks are provided, the networks must contain the same nodes and the same modules (the connections within modules may differ). Any connection that is common across different networks will also have the same partial correlation weight across networks.
gen_partial_correlations( ..., k = 2.5, rweights = function(n) (-1)^rbinom(n, 1, 0.5) * runif(n, 0.5, 1) )gen_partial_correlations( ..., k = 2.5, rweights = function(n) (-1)^rbinom(n, 1, 0.5) * runif(n, 0.5, 1) )
... |
The 'network' objects to modify. |
k |
An positive number used to ensure that the matrix inverse is
numerically stable. |
rweights |
A generator for initial weights in the network. By default, values are generated uniformly from (-1, -0.5) U (0.5, 1). The weights will be adjusted so that the sign of a generated weight and the sign of the corresponding partial correlation agree. |
An updated network object containing random weights. If multiple networks were provided, then a list of network objects is returned.
Grimes T, Datta S (2021). “SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.” Journal of Statistical Software, 98(12), 1–49. doi:10.18637/jss.v098.i12.
nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network.nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network.
The expression data are generated based on the gene-gene associations of an underlying network. An association structure is imposed by first generating data from a multivariate Gaussian distribution. Those data are then used to sample from the empirical distribution of gene expression profiles in the reference dataset using the inverse transform method.
gen_rnaseq(n, network, reference = NULL, verbose = TRUE)gen_rnaseq(n, network, reference = NULL, verbose = TRUE)
n |
The number of samples to generate. |
network |
A 'network' object or list of 'network' objects. |
reference |
A data.frame containing reference gene expression data. Rows
should correspond to samples and columns to genes. If |
verbose |
Boolean indicator for message output. |
A list containing the simulated expression data and the reference dataset. If a list of networks were provided, then the results for each network are returned as a list.
Grimes T, Datta S (2021). “SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.” Journal of Statistical Software, 98(12), 1–49. doi:10.18637/jss.v098.i12.
nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network. # If no reference is provided, the internal RNA-seq reference dataset is used. x <- gen_rnaseq(20, nw)$x # Simulate 20 observations from the network.nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network. # If no reference is provided, the internal RNA-seq reference dataset is used. x <- gen_rnaseq(20, nw)$x # Simulate 20 observations from the network.
The count data are generated based on the gene-gene associations of an
udnerlying network. An association structure is imposed by first generating
data from a multivariate Gaussian distribution, and counts are then obtained
through the inverse tranformation method. To generate realistic counts, either
a reference dataset or parameters for the ZINB model (size, mu, rho) can be
provided. Parameters can be estimated from a reference using the
est_params_from_reference function.
gen_zinb( n, network, reference = NULL, params = NULL, library_sizes = NULL, adjust_library_size = NULL, verbose = TRUE )gen_zinb( n, network, reference = NULL, params = NULL, library_sizes = NULL, adjust_library_size = NULL, verbose = TRUE )
n |
The number of samples to generate. |
network |
A 'network' object or list of 'network' objects. |
reference |
Either a vector or data.frame of counts from a reference
gene expression profile. If a data.frame is provided, each column should
correspond to a gene. If both |
params |
A matrix of ZINB parameter values; each column should contain the size, mu, and rho parameters for a gene. |
library_sizes |
A vector of library sizes. Used only if |
adjust_library_size |
A boolean value. If |
verbose |
Boolean indicator for message output. |
A list containing the generated counts and the ZINB parameters used to create them. If a list of networks were provided, then the results for each network are returned as a list.
nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network. # If no reference is provided, ZINB data are generated using an internal reference. x <- gen_zinb(20, nw)$x # Simulate 20 ZINB observations from the network.nw <- random_network(10) # Create a random network with 10 nodes. nw <- gen_partial_correlations(nw) # Add weights to connections in the network. # If no reference is provided, ZINB data are generated using an internal reference. x <- gen_zinb(20, nw)$x # Simulate 20 ZINB observations from the network.
The adjacency matrix is constructed from all modules in a network.
get_adjacency_matrix(x, ...)get_adjacency_matrix(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
An adjacency matrix with entry ij = 1 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
The connections in an adjacency matrix and association matrix may differ if the network contains multiple modules. The adjacency matrix only considers direct connections in the network, whereas the association matrix takes into account the fact that overlapping modules can create conditional dependencies between two genes in seperate modules (i.e. genes that don't have a direct connection in the graph).
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)
The adjacency matrix is constructed from all modules in a network.
## Default S3 method: get_adjacency_matrix(x, ...)## Default S3 method: get_adjacency_matrix(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
An adjacency matrix with entry ij = 1 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)
The adjacency matrix is constructed from all modules in a network.
## S3 method for class 'matrix' get_adjacency_matrix(x, ...)## S3 method for class 'matrix' get_adjacency_matrix(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
An adjacency matrix with entry ij = 1 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)
The adjacency matrix is constructed from all modules in a network.
## S3 method for class 'network' get_adjacency_matrix(x, ...)## S3 method for class 'network' get_adjacency_matrix(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
An adjacency matrix with entry ij = 1 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)
The adjacency matrix is constructed from all modules in a network.
## S3 method for class 'network_module' get_adjacency_matrix(x, ...)## S3 method for class 'network_module' get_adjacency_matrix(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
An adjacency matrix with entry ij = 1 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_adjacency_matrix(nw) module <- nw$modules[[1]] get_adjacency_matrix(module)
Get association matrix
get_association_matrix(x, tol = 10^-13, ...)get_association_matrix(x, tol = 10^-13, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
tol |
A small tolerance threshold; any entry that is within |
... |
Additional arguments. |
An association matrix with entry ij != 0 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
The connections in an adjacency matrix and association matrix may differ if the network contains multiple modules. The adjacency matrix only considers direct connections in the network, whereas the association matrix takes into account the fact that overlapping modules can create conditional dependencies between two genes in seperate modules (i.e. genes that don't have a direct connection in the graph).
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)
Get association matrix
## Default S3 method: get_association_matrix(x, tol = 10^-13, ...)## Default S3 method: get_association_matrix(x, tol = 10^-13, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
tol |
A small tolerance threshold; any entry that is within |
... |
Additional arguments. |
An association matrix with entry ij != 0 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)
Get association matrix
## S3 method for class 'matrix' get_association_matrix(x, tol = 10^-13, ...)## S3 method for class 'matrix' get_association_matrix(x, tol = 10^-13, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
tol |
A small tolerance threshold; any entry that is within |
... |
Additional arguments. |
An association matrix with entry ij != 0 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)
Get association matrix
## S3 method for class 'network' get_association_matrix(x, tol = 10^-13, ...)## S3 method for class 'network' get_association_matrix(x, tol = 10^-13, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
tol |
A small tolerance threshold; any entry that is within |
... |
Additional arguments. |
An association matrix with entry ij != 0 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)
Get association matrix
## S3 method for class 'network_module' get_association_matrix(x, tol = 10^-13, ...)## S3 method for class 'network_module' get_association_matrix(x, tol = 10^-13, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
tol |
A small tolerance threshold; any entry that is within |
... |
Additional arguments. |
An association matrix with entry ij != 0 if node i and j are connected, and 0 otherwise. The diagonal entries are all zero.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get adjacency matrix for the network or individual modules in the network. get_association_matrix(nw) module <- nw$modules[[1]] get_association_matrix(module)
Counts the connections to each node within each structure. Note, this is not the same as the degree distribution from the adjacency matrix obtained from the network, which collapses the individual structures into one graph.
get_degree_distribution(network)get_degree_distribution(network)
network |
A network object. |
A vector of length p, containing the degree for each node in the network.
set.seed(13245) nw <- random_network(10) deg <- get_degree_distribution(nw) # Degree of each node. table(deg) # Frequency table of degrees. # Five nodes have degree 2, three nodes have degree 3, etc.set.seed(13245) nw <- random_network(10) deg <- get_degree_distribution(nw) # Degree of each node. table(deg) # Frequency table of degrees. # Five nodes have degree 2, three nodes have degree 3, etc.
Get edge weights.
get_edge_weights_from_module(module)get_edge_weights_from_module(module)
module |
The 'network_module' object to get edge weights for. |
A vector containing the weights of each edge. If the edges are
unweighted, then a vector of 1's is returned. If there are no edges, in the
module, then NULL is returned.
nw <- random_network(10) nw <- gen_partial_correlations(nw) module <- nw$modules[[1]] get_edge_weights_from_module(module)nw <- random_network(10) nw <- gen_partial_correlations(nw) module <- nw$modules[[1]] get_edge_weights_from_module(module)
Internal function used to create coordinates based on a set of modules
get_layout_for_modules(g, modules)get_layout_for_modules(g, modules)
g |
An 'igraph' object |
modules |
A list containing sets of indicies indicating the nodes in
|
A matrix of coordinates for plotting
The average degree, clustering coefficient, and average path length are calculated.
get_network_characteristics(network, global_only = FALSE)get_network_characteristics(network, global_only = FALSE)
network |
A 'network', 'network_module', or 'matrix' object. |
global_only |
If |
A list containing characteristics of the network.
nw <- random_network(10) get_network_characteristics(nw)nw <- random_network(10) get_network_characteristics(nw)
Get a list of modules from the network
get_network_modules(network)get_network_modules(network)
network |
A 'network' object. |
A list whose length is the number of modules in the network; each element is a vector containing the indicies of the nodes that belong to that module.
set.seed(12345) # Create a random network of 50 nodes and modules of sizes between 5-20. nw <- random_network(50, n_modules = 5, min_module_size = 5, max_module_size = 20, avg_module_size = 10, sd_module_size = 5) get_network_modules(nw) # Indicies of nodes contained in each module.set.seed(12345) # Create a random network of 50 nodes and modules of sizes between 5-20. nw <- random_network(50, n_modules = 5, min_module_size = 5, max_module_size = 20, avg_module_size = 10, sd_module_size = 5) get_network_modules(nw) # Indicies of nodes contained in each module.
Get node names
get_node_names(x, ...)get_node_names(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A vector containing the node names or node indices.
Modules do not retain the names of each node, so the node indicies are returned instead. These can be used to index into the vector of node names obtained from the network.
# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)
Get node names
## Default S3 method: get_node_names(x, ...)## Default S3 method: get_node_names(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A vector containing the node names or node indices.
# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)
Get node names
## S3 method for class 'matrix' get_node_names(x, ...)## S3 method for class 'matrix' get_node_names(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A vector containing the node names or node indices.
# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)
Get node names
## S3 method for class 'network' get_node_names(x, ...)## S3 method for class 'network' get_node_names(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A vector containing the node names or node indices.
# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)
Get node names
## S3 method for class 'network_module' get_node_names(x, ...)## S3 method for class 'network_module' get_node_names(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A vector containing the node names or node indices.
# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)
The associations in each module are taken as partial correlations, and the covariance matrix is calculated from these assuming that expression for gene i is the weighted average over each module using 1/sqrt(m_i) as the weight, where m_i is the number of modules containing gene i.
get_sigma(x, ...)get_sigma(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A covariance matrix.
If a matrix is provided, it is assumed to be a partial correlation matrix;
a warning is given in this case. To avoid the warning message, convert the
matrix into a network object using
create_network_from_association_matrix.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)
The associations in each module are taken as partial correlations, and the covariance matrix is calculated from these assuming that expression for gene i is the weighted average over each module using 1/sqrt(m_i) as the weight, where m_i is the number of modules containing gene i.
## Default S3 method: get_sigma(x, ...)## Default S3 method: get_sigma(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A covariance matrix.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)
The associations in each module are taken as partial correlations, and the covariance matrix is calculated from these assuming that expression for gene i is the weighted average over each module using 1/sqrt(m_i) as the weight, where m_i is the number of modules containing gene i.
## S3 method for class 'matrix' get_sigma(x, ...)## S3 method for class 'matrix' get_sigma(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A covariance matrix.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)
The associations in each module are taken as partial correlations, and the covariance matrix is calculated from these assuming that expression for gene i is the weighted average over each module using 1/sqrt(m_i) as the weight, where m_i is the number of modules containing gene i.
## S3 method for class 'network' get_sigma(x, ...)## S3 method for class 'network' get_sigma(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A covariance matrix.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)
The associations in each module are taken as partial correlations, and the covariance matrix is calculated from these assuming that expression for gene i is the weighted average over each module using 1/sqrt(m_i) as the weight, where m_i is the number of modules containing gene i.
## S3 method for class 'network_module' get_sigma(x, ...)## S3 method for class 'network_module' get_sigma(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
A covariance matrix.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) # Get covariance matrix for the network or individual modules in the network. get_sigma(nw) module <- nw$modules[[1]] get_sigma(module)
Get summary for a node in the network.
get_summary_for_node(node, network)get_summary_for_node(node, network)
node |
The node to summarize. Can be a character string corresponding to a name of a node in the network, or an integer value from 1 to p corresponding to the index of a node. |
network |
A network object. |
A list containing summary information for the node; this includes a vector of indicies to other nodes in the network it is connected to, and a vector of incidices to modules that contain the node.
set.seed(12345) nw <- random_network(100) get_summary_for_node(1, nw) # Node 1 is contained in modules 1 and 2, and it is connected to nodes # 2, 4, 11, 13, 23, and 29.set.seed(12345) nw <- random_network(100) get_summary_for_node(1, nw) # Node 1 is contained in modules 1 and 2, and it is connected to nodes # 2, 4, 11, 13, 23, and 29.
This function plots the given network as a heatmap to visualize its connections. If the network is weighted, then the heatmap will use greyscale colors to represent connection strengths; black squares correspond to the strongest connections, while lighter color squares are weaker connections.
heatmap_network( network, main = NULL, col = colorRampPalette(gray.colors(8, 0.1, 1))(50), ... )heatmap_network( network, main = NULL, col = colorRampPalette(gray.colors(8, 0.1, 1))(50), ... )
network |
Either a network object or association matrix of the network. |
main |
A string containing the title for the graph. |
col |
Color palatte used for heatmap. See |
... |
Additional arguments passed to |
The matrix used to create the heatmap.
set.seed(12345) nw <- random_network(10) nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) heatmap_network(nw, "Unweighted Network") nw <- gen_partial_correlations(nw) heatmap_network(nw, "Weighted Network")set.seed(12345) nw <- random_network(10) nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) heatmap_network(nw, "Unweighted Network") nw <- gen_partial_correlations(nw) heatmap_network(nw, "Weighted Network")
C++ implementation to check if a matrix is symmetric
is_symmetric_cpp(m, tol = 1e-12)is_symmetric_cpp(m, tol = 1e-12)
m |
A matrix to check. |
tol |
A Numeric scalar >= 0. Differences smaller than tol are ignored. |
Returns TRUE if the matrix is symmetric and FALSE otherwise.
Check if an object is weighted
is_weighted(x, ...)is_weighted(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments.
object are weighted by 0s and 1s, and returns |
A Boolean value indicating whether the input is weighted.
# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)
Check if an object is weighted
## Default S3 method: is_weighted(x, ...)## Default S3 method: is_weighted(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments.
object are weighted by 0s and 1s, and returns |
A Boolean value indicating whether the input is weighted.
# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)
Check if an object is weighted
## S3 method for class 'matrix' is_weighted(x, ...)## S3 method for class 'matrix' is_weighted(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments.
object are weighted by 0s and 1s, and returns |
A Boolean value indicating whether the input is weighted.
# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)
Check if an object is weighted
## S3 method for class 'network' is_weighted(x, ...)## S3 method for class 'network' is_weighted(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments.
object are weighted by 0s and 1s, and returns |
A Boolean value indicating whether the input is weighted.
# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)
Check if an object is weighted
## S3 method for class 'network_module' is_weighted(x, ...)## S3 method for class 'network_module' is_weighted(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments.
object are weighted by 0s and 1s, and returns |
A Boolean value indicating whether the input is weighted.
# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)# Create a random network with 10 nodes. nw <- random_network(10) # The network, and hence all of its modules, are unweighted. is_weighted(nw) sapply(nw$modules, is_weighted) # Add random weights to the connections. nw <- gen_partial_correlations(nw) # The network, and hence all of its modules, are now weighted. is_weighted(nw) sapply(nw$modules, is_weighted)
The network is perturbed by removing connections from hubs and/or rewiring
other nodes in the network. By default, one hub is turned off (i.e. its
connections are removed each with probability rewire_hub_prob = 0.5), and
no other nodes are changed. Hub nodes are defined as those having degree
above three standard deviations from the average degree, and nodes are
sampled from these to be turned off; if there are no hub nodes, then
those with the largest degree are turned off.
perturb_network( network, n_hubs = 1, n_nodes = 0, rewire_hub_prob = 0.5, rewire_other_prob = 0.5, ... )perturb_network( network, n_hubs = 1, n_nodes = 0, rewire_hub_prob = 0.5, rewire_other_prob = 0.5, ... )
network |
The network to modify. |
n_hubs |
The number of hub nodes to turn off. |
n_nodes |
The number of non-hub nodes to rewire. When rewiring, the degree of the node is unchanged. |
rewire_hub_prob |
The probability that a connection is removed from
a hub that is selected to be turned off. If |
rewire_other_prob |
The probability that a connection is rewired from
a non-hub that is selected for rewiring. If |
... |
Additional arguments passed to
|
The modified network.
# Create a random network, perturb the network, then plot the differential network. set.seed(12345) nw <- random_network(100) # Rewire 2 random hub genes and 10 other random genes: nw_diff <- perturb_network(nw, n_hubs = 2, n_nodes = 10) plot_network_diff(nw, nw_diff)# Create a random network, perturb the network, then plot the differential network. set.seed(12345) nw <- random_network(100) # Rewire 2 random hub genes and 10 other random genes: nw_diff <- perturb_network(nw, n_hubs = 2, n_nodes = 10) plot_network_diff(nw, nw_diff)
Plots the expression of two genes for visual assessment of association.
plot_gene_pair( x_list, geneA, geneB, method = "loess", se_alpha = 0.1, do_facet_wrap = FALSE, scales = "fixed" )plot_gene_pair( x_list, geneA, geneB, method = "loess", se_alpha = 0.1, do_facet_wrap = FALSE, scales = "fixed" )
x_list |
A named list containing one or more n by p gene expression profiles, one for each group or subpopulation under consideration. |
geneA |
The name of the first gene to plot. Must be either a character
string matching a column name in each matrix of |
geneB |
The name of the second gene to plot. Must be either a character
string matching a column name in each matrix of |
method |
Charater string either "lm" or "loess" used for plotting.
For no line, set |
se_alpha |
Sets transparancy of confidence interval around association trend line. Set to 0 to remove the confidence interval. |
do_facet_wrap |
If |
scales |
Only used if |
Returns the generated plot.
data(reference) rnaseq <- reference$rnaseq genes <- colnames(rnaseq) plot_gene_pair(rnaseq, genes[1], genes[2]) # Suppose we had multiple data frames. control <- rnaseq[1:100, 1:10] treatment1 <- rnaseq[101:200, 1:10] treatment2 <- rnaseq[201:250, 1:10] plot_gene_pair(list(ctrl = control, trt1 = treatment1, trt2 = treatment2), genes[1], genes[2], method = NA) plot_gene_pair(list(ctrl = control, trt = treatment1), genes[1], genes[2], do_facet_wrap = TRUE, method = "lm")data(reference) rnaseq <- reference$rnaseq genes <- colnames(rnaseq) plot_gene_pair(rnaseq, genes[1], genes[2]) # Suppose we had multiple data frames. control <- rnaseq[1:100, 1:10] treatment1 <- rnaseq[101:200, 1:10] treatment2 <- rnaseq[201:250, 1:10] plot_gene_pair(list(ctrl = control, trt1 = treatment1, trt2 = treatment2), genes[1], genes[2], method = NA) plot_gene_pair(list(ctrl = control, trt = treatment1), genes[1], genes[2], do_facet_wrap = TRUE, method = "lm")
This function plots a network and highlights the individual modules.
An attempt is made to layout the nodes so that any visual overlaps among modules
correspond to true overlaps in the network, however it is possible that
a node may appear to be in multiple modules in the visualization when it does
not actually belong to multiple modules. If the result of another plot is
provided using the compare_graph argument, then the layout of this network
will be based on that plot and convex hulls are drawn to trace out the modules;
in this case it is likely that the displayed modules will contain extraneous
nodes.
plot_modules( network, compare_graph = NULL, as_subgraph = TRUE, modules = NULL, node_scale = 4, edge_scale = 1, node_color = adjustcolor("orange", 0.5), group_color = palette.colors(9, "Set 1"), generate_layout = igraph::nicely, include_vertex_labels = TRUE, show_legend = FALSE, legend_position = "topright", legend_horizontal = FALSE, display_plot = TRUE, ... )plot_modules( network, compare_graph = NULL, as_subgraph = TRUE, modules = NULL, node_scale = 4, edge_scale = 1, node_color = adjustcolor("orange", 0.5), group_color = palette.colors(9, "Set 1"), generate_layout = igraph::nicely, include_vertex_labels = TRUE, show_legend = FALSE, legend_position = "topright", legend_horizontal = FALSE, display_plot = TRUE, ... )
network |
A 'network' object to plot. Alternatively, an adjacency or association matrix can be provided, in which case the 'modules' argument should be specified. |
compare_graph |
The plot of another network to use for comparison. |
as_subgraph |
If |
modules |
A list of modules for the network; this is used to provide
a member list of each module when the |
node_scale |
Used for scaling of nodes. |
edge_scale |
Used for scaling of edges. |
node_color |
The color used for the nodes. |
group_color |
A vector of colors used for the modules. |
generate_layout |
A function to generate the layout of a graph; used
if coords is |
include_vertex_labels |
If |
show_legend |
If |
legend_position |
The location of the legend. Can be any one of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" or "center". |
legend_horizontal |
If |
display_plot |
If |
... |
Additional arguments passed to |
A 'network_plot' object for the network. This object can be passed
back into a future call of plot.network through the
compare_graph argument, which will setup the plot for easier
comparison between the old graph and the new graph of network.
set.seed(1) # Networks can be plotted with modules highlighted. nw <- random_network(100) g <- plot_network(nw) plot_modules(nw, g) # Overlay convex hulls around modules in previous layout.set.seed(1) # Networks can be plotted with modules highlighted. nw <- random_network(100) g <- plot_network(nw) plot_modules(nw, g) # Overlay convex hulls around modules in previous layout.
This function is used to plot a network. The network argument can be a
network object, network module, an adjacency matrix, or an association matrix.
If the result of another plot is provided using the compare_graph argument,
then the layout of this network will be based on that plot.
plot_network( network, compare_graph = NULL, as_subgraph = FALSE, node_scale = 4, edge_scale = 1, node_color = adjustcolor("orange", 0.5), generate_layout = igraph::nicely, include_vertex_labels = TRUE, display_plot = TRUE, ... )plot_network( network, compare_graph = NULL, as_subgraph = FALSE, node_scale = 4, edge_scale = 1, node_color = adjustcolor("orange", 0.5), generate_layout = igraph::nicely, include_vertex_labels = TRUE, display_plot = TRUE, ... )
network |
A 'network', 'network_module', or 'matrix' object. |
compare_graph |
The plot of another network to use for comparison. |
as_subgraph |
If |
node_scale |
Used for scaling of nodes. |
edge_scale |
Used for scaling of edges. |
node_color |
The color used for the nodes. |
generate_layout |
A function to generate the layout of a graph; used
if coords is |
include_vertex_labels |
If |
display_plot |
If |
... |
Additional arguments passed to |
Creates a plot of the network and returns a graph object.
The graph object can be passed back into a future call of
plot.network through the compare_edge argument, which
will setup the plot for easier comparison between the old graph and the graph
of network.
set.seed(0) # Basic plotting for networks, modules, and matricies nw <- random_network(10) plot(nw) module <- random_module(1:10) plot(module) adj_mat <- get_adjacency_matrix(nw) plot_network(adj_mat) # To compare multiple networks, the layout from the first plot can be used # in subsequent plots using the second argument, `compare_graph`. nw1 <- random_network(10) nw2 <- remove_connections_to_node(nw1, 6, prob_remove = 1) g <- plot(nw1) plot(nw2, g) # If the network contains many nodes of degree 0, plotting as subgraph # may be preferred. nw <- random_network(100, n_modules = 1) plot(nw) plot(nw, as_subgraph = TRUE) # Networks can be plotted with modules highlighted. nw <- random_network(100) g <- plot_network(nw) plot_modules(nw, g) # For large networks, the vertex labels can clutter the graph; these can # be removed using the `include_vertex_labels` argument. nw <- random_network(250) g <- plot(nw) plot(nw, g, include = FALSE)set.seed(0) # Basic plotting for networks, modules, and matricies nw <- random_network(10) plot(nw) module <- random_module(1:10) plot(module) adj_mat <- get_adjacency_matrix(nw) plot_network(adj_mat) # To compare multiple networks, the layout from the first plot can be used # in subsequent plots using the second argument, `compare_graph`. nw1 <- random_network(10) nw2 <- remove_connections_to_node(nw1, 6, prob_remove = 1) g <- plot(nw1) plot(nw2, g) # If the network contains many nodes of degree 0, plotting as subgraph # may be preferred. nw <- random_network(100, n_modules = 1) plot(nw) plot(nw, as_subgraph = TRUE) # Networks can be plotted with modules highlighted. nw <- random_network(100) g <- plot_network(nw) plot_modules(nw, g) # For large networks, the vertex labels can clutter the graph; these can # be removed using the `include_vertex_labels` argument. nw <- random_network(250) g <- plot(nw) plot(nw, g, include = FALSE)
This function plots the difference in connectivity between two networks.
For two identical networks, the graph will be empty. For non-identical
networks, black edges are shared by both networks but differ in magnitude or
direction (if the networks are weighted), tan edges are in network_1
but not network_2, and red edges are in network_2 but not
network_1. All edges are scaled according to the strongest association
in either network.
plot_network_diff( network_1, network_2, compare_graph = NULL, as_subgraph = FALSE, node_scale = 4, edge_scale = 1, node_color = adjustcolor("orange", 0.5), edge_colors = c("black", "wheat", "red"), generate_layout = igraph::nicely, include_vertex_labels = TRUE, ... )plot_network_diff( network_1, network_2, compare_graph = NULL, as_subgraph = FALSE, node_scale = 4, edge_scale = 1, node_color = adjustcolor("orange", 0.5), edge_colors = c("black", "wheat", "red"), generate_layout = igraph::nicely, include_vertex_labels = TRUE, ... )
network_1 |
A 'network' or 'matrix' object. |
network_2 |
A 'network' or 'matrix' object. |
compare_graph |
The plot of another network to use for comparison. |
as_subgraph |
If TRUE, only nodes of positive degree will be shown. |
node_scale |
Used for scaling of nodes. |
edge_scale |
Used for scaling of edges. |
node_color |
The color used for the nodes. |
edge_colors |
A vector of three colors used for edges; the first colors
edges common to both network, the second colors edges in |
generate_layout |
A function to generate the layout of a graph; used
if coords is |
include_vertex_labels |
If |
... |
Additional arguments passed to |
Creates a plot of the network and returns a graph object.
The graph object can be passed back into a future call of
plot_network, plot_network_diff, or
plot_network_sim through the compare_edge argument,
which will setup the plot for easier comparison between the old graph and
the graph of network.
# Create two networks, the second being a perturbation of the first. nw1 <- random_network(20) nw2 <- perturb_network(nw1, n_nodes = 5) # Can compare networks by plotting each using the same layout. g <- plot(nw1) plot(nw2, g) # Or, the differential network can be plotted. plot_network_diff(nw1, nw2, g)# Create two networks, the second being a perturbation of the first. nw1 <- random_network(20) nw2 <- perturb_network(nw1, n_nodes = 5) # Can compare networks by plotting each using the same layout. g <- plot(nw1) plot(nw2, g) # Or, the differential network can be plotted. plot_network_diff(nw1, nw2, g)
This function plots the similarity of connections between two networks.
Both networks must be weighted. The width of each edge corresponds to
the strength of similarity and is calculated by sqrt(abs((s1 + s2)s1s2)),
where s1 and s2 are the weights for a particular
connection in network_1 and network_2, respectively
plot_network_sim(network_1, network_2, compare_graph = NULL, ...)plot_network_sim(network_1, network_2, compare_graph = NULL, ...)
network_1 |
A weighted 'network' or 'matrix' object. |
network_2 |
A weighted 'network' or 'matrix' object. |
compare_graph |
The plot of another network to use for comparison. |
... |
Additional arguments passed to |
Creates a plot of the network and returns a graph object.
The graph object can be passed back into a future call of
plot_network, plot_network_diff or
plot_network_sim through the compare_edge argument,
which will setup the plot for easier comparison between the old graph and
the graph of network.
# Create two networks, the second being a perturbation of the first. nw1 <- random_network(20) nw2 <- perturb_network(nw1, n_nodes = 5) nw1 <- gen_partial_correlations(nw1) nw2 <- gen_partial_correlations(nw2) # Can compare networks by plotting each using the same layout. g <- plot(nw1) plot(nw2, g) # Or, plot the differential network or similarity network plot_network_diff(nw1, nw2, g) plot_network_sim(nw1, nw2, g) # Note the behavior when both networks are the same. plot_network_diff(nw1, nw1, g) # No differences produces an empty network plot_network_sim(nw1, nw1, g) # Edge widths are still scaled by connection strength.# Create two networks, the second being a perturbation of the first. nw1 <- random_network(20) nw2 <- perturb_network(nw1, n_nodes = 5) nw1 <- gen_partial_correlations(nw1) nw2 <- gen_partial_correlations(nw2) # Can compare networks by plotting each using the same layout. g <- plot(nw1) plot(nw2, g) # Or, plot the differential network or similarity network plot_network_diff(nw1, nw2, g) plot_network_sim(nw1, nw2, g) # Note the behavior when both networks are the same. plot_network_diff(nw1, nw1, g) # No differences produces an empty network plot_network_sim(nw1, nw1, g) # Edge widths are still scaled by connection strength.
This function plots the given network. If the result of another plot is provided, this plot will be modified for easier comparison.
## S3 method for class 'network' plot(x, compare_graph = NULL, show_modules = FALSE, as_subgraph = FALSE, ...)## S3 method for class 'network' plot(x, compare_graph = NULL, show_modules = FALSE, as_subgraph = FALSE, ...)
x |
A 'network' object. |
compare_graph |
The plot of another network to use for comparison. |
show_modules |
If |
as_subgraph |
If |
... |
Additional arguments passed to |
Creates a plot of the module and returns a graph object.
See plot_modules and plot_network for details.
A 'network_plot' object for the network. This object can be passed
back into a future call of plot.network through the
compare_graph argument, which will setup the plot for easier
comparison between the old graph and the new graph of network.
nw <- random_network(10) plot(nw)nw <- random_network(10) plot(nw)
Plot function for 'network_module' object.
## S3 method for class 'network_module' plot(x, ...)## S3 method for class 'network_module' plot(x, ...)
x |
A 'network_module' object. |
... |
Additional arguments passed to |
Creates a plot of the module and returns a graph object.
See plot_network for details.
module <- random_module(1:10) plot(module)module <- random_module(1:10) plot(module)
Plot function for 'network_plot' class
## S3 method for class 'network_plot' plot(x, ...)## S3 method for class 'network_plot' plot(x, ...)
x |
A 'network_plot' object obtained from |
... |
Additional arguments passed to |
Creates a plot of the network and returns a graph object.
See plot_network for details.
nw <- random_network(10) g <- plot(nw) # Can change the plot by modifying the instance `g`. # For example, make vertex size and edge width twice as big. g$edge.width <- 2 * g$edge.width g$vertex.size <- 2 * g$vertex.size # Change color of verticies, edges, and vertex labels. g$edge.color <- "orange" g$vertex.color <- "navy" g$vertex.label.color <- "white" plot(g)nw <- random_network(10) g <- plot(nw) # Can change the plot by modifying the instance `g`. # For example, make vertex size and edge width twice as big. g$edge.width <- 2 * g$edge.width g$vertex.size <- 2 * g$vertex.size # Change color of verticies, edges, and vertex labels. g$edge.color <- "orange" g$vertex.color <- "navy" g$vertex.label.color <- "white" plot(g)
Print function for 'network' object.
## S3 method for class 'network' print(x, ...)## S3 method for class 'network' print(x, ...)
x |
A 'network' object. |
... |
Additional arguments are ignored. |
Prints a summary of the module.
nw <- random_network(10) nw print(nw)nw <- random_network(10) nw print(nw)
Print function for 'network_module' object.
## S3 method for class 'network_module' print(x, ...)## S3 method for class 'network_module' print(x, ...)
x |
A 'network_module' object. |
... |
Additional arguments are ignored. |
Prints a summary of the module.
module <- random_module(1:10) module print(module)module <- random_module(1:10) module print(module)
Displays the network plot.
## S3 method for class 'network_plot' print(x, ...)## S3 method for class 'network_plot' print(x, ...)
x |
A 'network_plot' object obtained from |
... |
Additional arguments passed to |
Creates a plot of the network and returns a graph object.
See plot_network for details.
nw <- random_network(10) g <- plot(nw, display_plot = FALSE) # Doesn't display the plot. g # Displays the plot.nw <- random_network(10) g <- plot(nw, display_plot = FALSE) # Doesn't display the plot. g # Displays the plot.
The Zero-Inflated Negative Binomial Distribution
pzinb(q, size, mu, rho, lower.tail = TRUE, log.p = FALSE)pzinb(q, size, mu, rho, lower.tail = TRUE, log.p = FALSE)
q |
A vector of quantities. |
size |
The dispersion paramater used in |
mu |
The mean parameter used in |
rho |
The zero-inflation parameter. |
lower.tail |
Logical; if |
log.p |
Logical; if |
The probabilities for the given q values.
x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))
The Zero-Inflated Negative Binomial Distribution
qzinb(p, size, mu, rho, lower.tail = TRUE, log.p = FALSE)qzinb(p, size, mu, rho, lower.tail = TRUE, log.p = FALSE)
p |
A vector of probabilities |
size |
The dispersion paramater used in |
mu |
The mean parameter used in |
rho |
The zero-inflation parameter. |
lower.tail |
Logical; if |
log.p |
Logical; if |
The quantiles for the given probabilities.
x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))
Create a random module
random_module(nodes, module_name = NULL, ...)random_module(nodes, module_name = NULL, ...)
nodes |
A numeric vector indicating which nodes in the network are contained in this module. |
module_name |
(optional) Character string specifying the name of the
module. If |
... |
Additional arguments passed to
|
A 'network_module' object.
Grimes T, Datta S (2021). “SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.” Journal of Statistical Software, 98(12), 1–49. doi:10.18637/jss.v098.i12.
module <- random_module(1:10)module <- random_module(1:10)
A single, connected graph is created. The graph is initialized as a ring lattice, and edges are randomly rewired and/or removed. The procedure is similar to the Watts-Strogatz method, but the sampling of edges to modify can be based on the degree of each node.
random_module_structure( size, prob_rewire = 1, prob_remove = 0.5, weights = NULL, neig_size = 3, alpha = 100, beta = 1, epsilon = 10^-5, ... )random_module_structure( size, prob_rewire = 1, prob_remove = 0.5, weights = NULL, neig_size = 3, alpha = 100, beta = 1, epsilon = 10^-5, ... )
size |
The number of nodes to include in the graph. |
prob_rewire |
The probability of rewiring an edge. |
prob_remove |
The probability of removing an edge. |
weights |
(Optional) Weights used for sampling nodes. See
|
neig_size |
The neighborhood size within which the nodes of the
ring lattice are connected. The initial degree of each node is
|
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
... |
Additional arguments are ignored. |
An adjacency matrix representing the network structure.
# Create a random module structure (an adjacency matrix) for 10 nodes. adj_mat <- random_module_structure(10) # A network object can be created using this structure. module <- create_module_from_adjacency_matrix(adj_mat) nw <- create_network_from_modules(10, module)# Create a random module structure (an adjacency matrix) for 10 nodes. adj_mat <- random_module_structure(10) # A network object can be created using this structure. module <- create_module_from_adjacency_matrix(adj_mat) nw <- create_network_from_modules(10, module)
Creates an unweighted 'network' object containing randomly generated modules.
random_network(p, n_modules = NULL, ...)random_network(p, n_modules = NULL, ...)
p |
The number of nodes in the network. If |
|||||||||||||||||||||||
n_modules |
The number of modules to include in the network. If
|
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... |
Arguments to be passed to other methods. Possible arguments include:
|
An unweighted network object.
Grimes T, Datta S (2021). “SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.” Journal of Statistical Software, 98(12), 1–49. doi:10.18637/jss.v098.i12.
# Create a random network of 10 nodes nw <- random_network(10) nw # Add a random weight to each connection. nw <- gen_partial_correlations(nw) # Plot the network plot(nw)# Create a random network of 10 nodes nw <- random_network(10) nw # Add a random weight to each connection. nw <- gen_partial_correlations(nw) # Plot the network plot(nw)
The reference is a breast invasive carcinoma dataset containing gene expression profiles generated by The Cancer Genome Atlas (TCGA) and downloaded using the LinkedOmics portal. The dataset contains 1093 samples and 15944 genes. The reference is a list containing a data frame of the expression data and a data frame of estimated ZINB parameters for each expression profile.
referencereference
A list containing two data frames:
A 1093 by 15944 data frame containing the raw RNA-seq expression counts
A 3 by 15944 data frame containing the estimated ZINB parameters for each expression profile
http://www.linkedomics.org/data_download/TCGA-BRCA/
Remove connections in a network
remove_connections(x, prob_remove, run_checks = TRUE, ...)remove_connections(x, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_remove |
A value between 0 and 1. Each edge will be removed with
probability equal to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections to a node
remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to unwire. |
prob_remove |
A value between 0 and 1. Each connection to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections to a node
## Default S3 method: remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)## Default S3 method: remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to unwire. |
prob_remove |
A value between 0 and 1. Each connection to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections to a node
## S3 method for class 'matrix' remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)## S3 method for class 'matrix' remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to unwire. |
prob_remove |
A value between 0 and 1. Each connection to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections to a node
## S3 method for class 'network' remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)## S3 method for class 'network' remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to unwire. |
prob_remove |
A value between 0 and 1. Each connection to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections to a node
## S3 method for class 'network_module' remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)## S3 method for class 'network_module' remove_connections_to_node(x, node, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to unwire. |
prob_remove |
A value between 0 and 1. Each connection to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(10) # Remove all connections to node 1. nw_rewired <- remove_connections_to_node(nw, 1, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections in a network
## Default S3 method: remove_connections(x, prob_remove, run_checks = TRUE, ...)## Default S3 method: remove_connections(x, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_remove |
A value between 0 and 1. Each edge will be removed with
probability equal to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections in a network
## S3 method for class 'matrix' remove_connections(x, prob_remove, run_checks = TRUE, ...)## S3 method for class 'matrix' remove_connections(x, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_remove |
A value between 0 and 1. Each edge will be removed with
probability equal to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections in a network
## S3 method for class 'network' remove_connections(x, prob_remove, run_checks = TRUE, ...)## S3 method for class 'network' remove_connections(x, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_remove |
A value between 0 and 1. Each edge will be removed with
probability equal to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Remove connections in a network
## S3 method for class 'network_module' remove_connections(x, prob_remove, run_checks = TRUE, ...)## S3 method for class 'network_module' remove_connections(x, prob_remove, run_checks = TRUE, ...)
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_remove |
A value between 0 and 1. Each edge will be removed with
probability equal to |
run_checks |
If |
... |
Additional arguments. |
The modified adjacency matrix.
# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)# Create a random network with 10 nodes. nw <- random_network(20) # Remove connections in the network each with probability 1/2. nw_rewired <- remove_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired)
Removes the weights of all connections
remove_weights(x, ...)remove_weights(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)
Removes the weights of all connections
## Default S3 method: remove_weights(x, ...)## Default S3 method: remove_weights(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)
Removes the weights of all connections
## S3 method for class 'matrix' remove_weights(x, ...)## S3 method for class 'matrix' remove_weights(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)
Removes the weights of all connections
## S3 method for class 'network' remove_weights(x, ...)## S3 method for class 'network' remove_weights(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)
Removes the weights of all connections
## S3 method for class 'network_module' remove_weights(x, ...)## S3 method for class 'network_module' remove_weights(x, ...)
x |
Either a 'network', 'network_module', or 'matrix' object. |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)# Create a random network with 10 nodes and add random edge weights. nw <- random_network(10) nw <- gen_partial_correlations(nw) is_weighted(nw) # Remove the edge weights from the network. nw <- remove_weights(nw) is_weighted(nw)
Internal function for replacing a module in the network
replace_module_in_network(module_index, module, network)replace_module_in_network(module_index, module, network)
module_index |
The index of the module to replace. |
module |
The new module to replace with. |
network |
The network to modify. |
The modified network.
Rewire connections
rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_rewire |
A value between 0 and 1. The connections to each node
will be rewired with probability equal to |
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling a node to rewire to. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified module.
When applied to a network object, all modules in the network are rewired. If
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections to a node
rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to rewire. |
prob_rewire |
A value between 0 and 1, inclusive. Each connection to
|
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling nodes to rewire. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections to a node
## Default S3 method: rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## Default S3 method: rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to rewire. |
prob_rewire |
A value between 0 and 1, inclusive. Each connection to
|
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling nodes to rewire. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections to a node
## S3 method for class 'matrix' rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## S3 method for class 'matrix' rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to rewire. |
prob_rewire |
A value between 0 and 1, inclusive. Each connection to
|
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling nodes to rewire. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections to a node
## S3 method for class 'network' rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## S3 method for class 'network' rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to rewire. |
prob_rewire |
A value between 0 and 1, inclusive. Each connection to
|
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling nodes to rewire. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections to a node
## S3 method for class 'network_module' rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## S3 method for class 'network_module' rewire_connections_to_node( x, node, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
node |
The node to rewire. |
prob_rewire |
A value between 0 and 1, inclusive. Each connection to
|
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling nodes to rewire. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified object.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire connections to the first node. nw_rewired <- rewire_connections_to_node(nw, 1) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections
## Default S3 method: rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## Default S3 method: rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_rewire |
A value between 0 and 1. The connections to each node
will be rewired with probability equal to |
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling a node to rewire to. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified module.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections
## S3 method for class 'matrix' rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## S3 method for class 'matrix' rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_rewire |
A value between 0 and 1. The connections to each node
will be rewired with probability equal to |
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling a node to rewire to. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified module.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections
## S3 method for class 'network' rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## S3 method for class 'network' rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_rewire |
A value between 0 and 1. The connections to each node
will be rewired with probability equal to |
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling a node to rewire to. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified module.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
Rewire connections
## S3 method for class 'network_module' rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )## S3 method for class 'network_module' rewire_connections( x, prob_rewire = 1, weights = NULL, alpha = 100, beta = 1, epsilon = 10^-5, run_checks = TRUE, ... )
x |
The 'network', 'network_module', or 'matrix' object to modify. |
prob_rewire |
A value between 0 and 1. The connections to each node
will be rewired with probability equal to |
weights |
(Optional) A vector of weights for each node. These are used in addition to the degree of each node when sampling a node to rewire to. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
A small constant added to the sampling probability of each node. |
run_checks |
If |
... |
Additional arguments. |
The modified module.
# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)# Create a random network with 10 nodes. nw <- random_network(10) # Rewire nodes in the network each with probability 1/2 nw_rewired <- rewire_connections(nw, 0.5) # Plot the two networks for comparison g <- plot(nw) plot(nw_rewired, g) # Pass in g to mirror the layout. # Or plot the differential network. plot_network_diff(nw, nw_rewired, g)
C++ implementation for creating a ring lattice
ring_lattice_cpp(p, neig_size)ring_lattice_cpp(p, neig_size)
p |
The number of nodes in the lattice. |
neig_size |
The neighborhood side within which nodes are connected. |
Returns the adjacency matrix for the ring lattice.
The Zero-Inflated Negative Binomial Distribution
rzinb(n, size, mu, rho)rzinb(n, size, mu, rho)
n |
The number of random values to return. |
size |
The dispersion paramater used in |
mu |
The mean parameter used in |
rho |
The zero-inflation parameter. |
The randomly generated values from the distribution.
x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))x <- rzinb(10, 1, 10, 0.1) p <- pzinb(x, 1, 10, 0.1) y <- qzinb(p, 1, 10, 0.1) all(x == y) # Compute P(0 < X < 5) for X ~ ZINB(1, 10, 0.1) sum(dzinb(0:5, 1, 10, 0.1))
Sample link nodes for new module
sample_link_nodes( n, nodes, degree, alpha = 100, beta = 1, epsilon = 10^-5, ... )sample_link_nodes( n, nodes, degree, alpha = 100, beta = 1, epsilon = 10^-5, ... )
n |
The number of link nodes to sample. |
nodes |
The nodes to sample from. |
degree |
The degree of each node. |
alpha |
A positive value used to parameterize the Beta distribution. |
beta |
A positive value used to parameterize the Beta distribution. |
epsilon |
Used when sampling link nodes. |
... |
Additional arguments are ignored. |
A vector of selected nodes (possibly of length 1).
This function is used by create_modules_for_network
and is not meant to be used externally.
Sample nodes for new module
sample_module_nodes(n, nodes, degree, nu = 0.01, ...)sample_module_nodes(n, nodes, degree, nu = 0.01, ...)
n |
The number of nodes to sample. |
nodes |
The nodes available to sample from. |
degree |
The degree of each node. |
nu |
Multiplier for nodes that are already in one or more modules. |
... |
Additional arguments are ignored. |
A vector of selected nodes of length m.
This function is used by create_modules_for_network
and is not meant to be used externally.
Sample genes from reference dataset
sample_reference_data(reference_data, p, percent_ZI = NULL, threshold_ZI = 0.2)sample_reference_data(reference_data, p, percent_ZI = NULL, threshold_ZI = 0.2)
reference_data |
The reference data.frame to use. |
p |
The number of genes (columns) to sample. |
percent_ZI |
The desired percentage of zero-inflated genes. This percentage of zero-inflated genes will be sampled from the reference dataset, and the remaining will be non-zero-inflated. If NULL, then genes are sampled at random from the reference dataset. |
threshold_ZI |
The minimum proportion of zero counts for a gene to be considered as zero inflated. This is used to identify which genes in the reference dataset are zero-inflated. |
The modified reference dataset.
If p is greater than the number of columns in the reference dataset, then sampling with replacement will be used (with a warning message).
data(reference) rnaseq <- reference$rnaseq rnaseq_subset <- sample_reference_data(rnaseq, 10)data(reference) rnaseq <- reference$rnaseq rnaseq_subset <- sample_reference_data(rnaseq, 10)
Internal function used to set the edges in a module
set_module_edges(module, edges)set_module_edges(module, edges)
module |
The 'network_module' object to modify. |
edges |
A matrix used to indicate the edges in the module. If the matrix is square and contains the same number of rows and columns as nodes in the module, then it is assumed to be an adjacency matrix and the nonzero lower-triangle values of the matrix are used to indicate edges in the module. If the matrix is not square, the first two columns are assumed to be an edge list. |
The modified 'network_module' object.
Set the name for a module
set_module_name(module, module_name)set_module_name(module, module_name)
module |
The 'network_module' object to modify. |
module_name |
A character string. |
The modified 'network_module' object.
nw <- random_network(10) nw <- gen_partial_correlations(nw) module <- nw$modules[[1]] named_module <- set_module_name(module, "new name")nw <- random_network(10) nw <- gen_partial_correlations(nw) module <- nw$modules[[1]] named_module <- set_module_name(module, "new name")
Internal function to set the connection weights for a module
set_module_weights(module, weights)set_module_weights(module, weights)
module |
The 'network_module' object to modify. |
weights |
A vector or matrix of weights for each connetions. If a vector, its length must equal the number of connections in the module. If a matrix, it should be square with the number of columns equal to the number of nodes in the module; only the entries in the lower triangle that correspond to connections in the module will be used. |
The modified 'network_module' object.
Set the node names in a network
set_node_names(network, node_names)set_node_names(network, node_names)
network |
The network to modify. |
node_names |
A vector of strings containing the names for each node
in the network. If a numeric vector is provided, the values will be coerced
into strings. If 'node_names' is |
The modified network.
# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)# Create a random network with 10 nodes. nw <- random_network(10) get_node_names(nw) # Default names are 1, 2, ..., 10. nw <- set_node_names(nw, paste("node", 1:10, sep = "_")) get_node_names(nw) # Print out updated node names. # Modules only contain the indicies to nodes, not the node names module <- nw$modules[[1]] get_node_names(module) # When converting the network to a matrix, node names appear as column names. adj_matrix <- get_adjacency_matrix(nw) colnames(adj_matrix)
The small-world network is generated using the Watts-Strogatz method.
See watts.strogatz.game for details.
update_module_with_random_weights( module, rdist = function(n) { runif(n, 0.5, 1) * (-1)^rbinom(n, 1, 0.5) }, ... )update_module_with_random_weights( module, rdist = function(n) { runif(n, 0.5, 1) * (-1)^rbinom(n, 1, 0.5) }, ... )
module |
The network_module object to modify. |
rdist |
A distribution function that generates random numbers. The first argument should specify the number of weights to generate. By default, weights are generated uniformly from the set (-1, -0.5)U(0.5, 1). |
... |
Additional parameters are ignored. |
An updated 'network_module' object.
# Create a random module. module <- random_module(1:10) is_weighted(module) # Add a random weight to each connection. module <- update_module_with_random_weights(module) is_weighted(module)# Create a random module. module <- random_module(1:10) is_weighted(module) # Add a random weight to each connection. module <- update_module_with_random_weights(module) is_weighted(module)