dyngen (version 0.4.0)

generate_feature_network: Generate a target network

Description

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.

Usage

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.

Examples

# NOT RUN {
model <- 
  initialise_model(
    backbone = backbone_bifurcating(),
    feature_network = feature_network_default(damping = 0.1)
  )
  
# }
# NOT RUN {
model <- model %>%
  generate_tf_network() %>%
  generate_feature_network()
  
plot_feature_network(model)
  
model <- model %>%
  generate_kinetics() %>%
  generate_gold_standard() %>%
  generate_cells() %>%
  generate_experiment()
dataset <- wrap_dataset(model)
# }