Rank TFs and genes in network

calculate_gene_rank(
  network_table,
  regulators = NULL,
  targets = NULL,
  directed = FALSE
)

Arguments

network_table: The weight data table of network.
regulators: Regulators list.
targets: Targets list.
directed: Whether the network is directed.

Value

A table of gene rank.

Examples

data("example_matrix")
network_table <- inferCSN(example_matrix)
#> ℹ Running for <dense matrix>.
#> ℹ Checking input parameters.
#> ℹ Using L0 sparse regression model.
#> ℹ Using 1 core
#> ✔ Run done.
head(calculate_gene_rank(network_table))
#>   gene rank_value regulator
#> 1  g18 0.05884077      TRUE
#> 2  g17 0.05873368      TRUE
#> 3   g9 0.05869206      TRUE
#> 4   g5 0.05863344      TRUE
#> 5   g7 0.05811124      TRUE
#> 6   g6 0.05756522      TRUE
head(calculate_gene_rank(network_table, regulators = "g1"))
#>   gene rank_value regulator
#> 1   g1 0.46396396      TRUE
#> 2   g2 0.18783716     FALSE
#> 3  g18 0.13390298     FALSE
#> 4  g17 0.02111786     FALSE
#> 5   g5 0.02038942     FALSE
#> 6   g9 0.01973070     FALSE
head(calculate_gene_rank(network_table, targets = "g1"))
#>   gene rank_value regulator
#> 1   g1 0.46396396     FALSE
#> 2  g18 0.22091630      TRUE
#> 3   g2 0.09045695      TRUE
#> 4  g17 0.03444956      TRUE
#> 5   g9 0.01902045      TRUE
#> 6  g16 0.01740632      TRUE