Switch network table to matrix
table_to_matrix(network_table, regulators = NULL, targets = NULL)Weight matrix
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(network_table)
#> regulator target weight
#> 1 g18 g1 -0.9223177
#> 2 g17 g18 0.8770468
#> 3 g4 g3 0.8103065
#> 4 g16 g15 0.7659245
#> 5 g17 g16 0.7558764
#> 6 g12 g11 0.7444053
table_to_matrix(network_table)[1:6, 1:6]
#> g1 g2 g3 g4 g5 g6
#> g1 0.00000000 0.66844490 0.0180879493 -0.03658957 0.04489503 -0.030280993
#> g2 0.35630356 0.00000000 0.5492431610 0.02103272 0.01366921 -0.007459899
#> g3 0.01235281 0.70713027 0.0000000000 0.68526346 0.12187906 -0.001181702
#> g4 -0.02896993 0.03201306 0.8103064829 0.00000000 0.65615667 0.124827620
#> g5 0.03866820 0.02176632 0.1538770195 0.70453814 0.00000000 0.655663735
#> g6 -0.02979658 -0.01242983 -0.0005067688 0.14958079 0.72762920 0.000000000
table_to_matrix(
network_table,
regulators = c("g1", "g2"),
targets = c("g3", "g4")
)
#> g3 g4
#> g1 0.01808795 -0.03658957
#> g2 0.54924316 0.02103272