generate_feature_network() generates a network of target genes that are regulated by the previously generated TFs, and also a separate network of housekeeping genes (HKs). feature_network_default() is used to configure parameters pertaining this process.

generate_feature_network(model)

feature_network_default(
realnet = NULL,
damping = 0.01,
target_resampling = Inf,
max_in_degree = 5
)

## Arguments

model A dyngen intermediary model for which the transcription network has been generated with generate_tf_network(). The name of a gene regulatory network (GRN) in realnets. If NULL, a random network will be sampled from realnets. Alternatively, a custom GRN can be used by passing a weighted sparse matrix. A damping factor used for the page rank algorithm used to subsample the realnet. How many targets / HKs to sample from the realnet per iteration. The maximum in-degree of a target / HK.

## Value

A dyngen model.

dyngen on how to run a complete dyngen simulation

## Examples

model <-
initialise_model(
backbone = backbone_bifurcating(),
feature_network = feature_network_default(damping = 0.1)
)

# \donttest{
data("example_model")
model <- example_model %>%
generate_tf_network() %>%
generate_feature_network()

plot_feature_network(model)
# }