gen_zinb: Generate ZINB counts from an underlying network
In SeqNet: Generate RNA-Seq Data from Gene-Gene Association Networks

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. 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
)

`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 `reference` 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_sizes` 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.

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.