Table 3:

Summary of the main SeqNet functions.

Function	Description
random_network()	Generate a random network containing p nodes (genes). Additional tuning parameters for the network generating algorithm can be used here, including nu, prob_rewire, prob_remove, and alpha.
as_single_module()	Used to coerce a network into a single, large module rather than a collection of overlapping modules. This affects how data are generated when passed into gen_rnaseq(), as discussed in Section 2.3.1. The decision to use this function should be based on the user’s model of the data generating process.
perturb_network()	Rewires a network to obtain a differential network. By default, a hub node is randomly sampled from the network, and each of its connections are removed with probability 0.5. Note, this function can be used iteratively to turn off many hub nodes.
gen_partial_correlations()	Generates weights for the connections in a network. When multiple network objects are provided, connection that are common across networks are given the same weight.
print()	Printing a network displays the number of nodes, edges, and modules, along with the average degree, clustering coefficient, and average path length in the network.
plot()	Generates a plot of the network. See also plot_network().
plot_network_diff()	Generates a plot of the differential network between two network objects.
gen_rnaseq()	Generate an RNA-seq dataset containing n samples (rows) with a dependence structure defined by a given network. A reference dataset can be provided.
sample_reference_data()	Helper function for sampling p genes (columns) from a reference dataset. The argument percent_ZI is useful when the reference contains zero-inflated genes to control the proportion of the p sampled genes that are zero-inflated.
get_adj acency_matrix()	Creates an adjacency matrix from a network. If genes i and j are connected in the network (in any module), then the ijth entry of the adjacency matrix is 1, and otherwise is 0.
create_network_from_adj acency_matrix()	Creates a network from an adjacency matrix. This is useful when the user has a prespecified network structure, i.e. doesn’t want to generate a random one, but does want to generate RNA-seq data.