Generate RNA-Seq Data from Gene-Gene Association Networks
Get a list of modules from the network
Get node names
Get node names
Get node names
Get node names
Get node names
Get the covariance matrix
Get the covariance matrix
Get the covariance matrix
Get the covariance matrix
Get the covariance matrix
Get summary for a node in the network.
Plot heatmap representation of a network
Internal function to check if a matrix is positive definite
C++ implementation to check if a matrix is symmetric
Check if an object is weighted
Check if an object is weighted
Check if an object is weighted
Check if an object is weighted
Check if an object is weighted
Perturbs the connections in a network
Plot function for 'network' object
Plot function for 'network_module' object.
Plot function for 'network_plot' class
Internal function for adding a set of modules to the network
Adds a random module of a given size to the network
Internal function to check if a list of networks all contain the same ...
Internal function to check if a list of networks all contain the same ...
Collapses all modules in network into a single module
C++ implementation to check if a matrix is an adjacency matrix
C++ implementation to obtain connected components in a graph.
Connect disconnected components in an adjacency matrix
Create an edge table file for Cytoscape
Create a module
Create a network object.
Create a module from an adjacency matrix
Create a module from an association matrix
Randomly sample subsets of genes for each module
Create a network object from an adjacency matrix
Create a network object from an association matrix
Create a network object.
The Zero-Inflated Negative Binomial Distribution
C++ implementation of empirical CDF
C++ implementation for obtaining an edge list from adjacency matrix
Estimate ZINB parameters from reference data
Generate observations from a Gaussian graphical model.
Generate partial correlations for a list of networks.
Generate RNA-seq data from an underlying network
Generate ZINB counts from an underlying network
Get adjacency matrix
Get adjacency matrix
Get adjacency matrix
Get adjacency matrix
Get adjacency matrix
Get association matrix
Get association matrix
Get association matrix
Get association matrix
Get association matrix
Get the degree distribution for a network.
Get edge weights.
Internal function used to create coordinates based on a set of modules
Internal function used to extract 'network' objects from argument list...
Characteristics of the network topology
Scatter plot of two gene expressions
Visualize a network and its modules
Visualize a network
Plot the difference between two networks
Plot the similarity between two networks
Print function for 'network' object.
Print function for 'network_module' object.
Print function for 'network_plot' class
The Zero-Inflated Negative Binomial Distribution
The Zero-Inflated Negative Binomial Distribution
Create a random module
Create a random network structure for a module
Create a network object.
Remove connections in a network
Remove connections in a network
Remove connections in a network
Remove connections in a network
Remove connections in a network
Remove connections to a node
Remove connections to a node
Remove connections to a node
Remove connections to a node
Remove connections to a node
Removes the weights of all connections
Removes the weights of all connections
Removes the weights of all connections
Removes the weights of all connections
Removes the weights of all connections
Internal function for replacing a module in the network
Rewire connections
Rewire connections
Rewire connections
Rewire connections
Rewire connections
Rewire connections to a node
Rewire connections to a node
Rewire connections to a node
Rewire connections to a node
Rewire connections to a node
C++ implementation for creating a ring lattice
The Zero-Inflated Negative Binomial Distribution
Sample link nodes for new module
Sample nodes for new module
Sample genes from reference dataset
Internal function used to set the edges in a module
Set the name for a module
Internal function to set the connection weights for a module
Set the node names in a network
Generate small-world network structure for module
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.