Description Usage Arguments Value Examples
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.
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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 'reference' and 'params' are NULL, then parameters are estimated from the kidney dataset. |
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 'reference' is NULL. |
adjust_library_size |
A boolean value. If TRUE, the library size of generated counts are adjusted based on the reference library sizes. If both 'reference' and 'library_size' is NULL, then no adjustment is made. By default, this adjustment is made if the necessary information is provided. |
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.
1 2 3 4 | 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.
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