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().- realnet
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.- damping
A damping factor used for the page rank algorithm used to subsample the realnet.
- target_resampling
How many targets / HKs to sample from the realnet per iteration.
- max_in_degree
The maximum in-degree of a target / HK.
Value
A dyngen model.
See also
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)
# }