Format network table
network_format(
network_table,
regulators = NULL,
targets = NULL,
abs_weight = TRUE
)
Formated network table
data("example_matrix")
network_table <- inferCSN(example_matrix)
#> ✔ Running for <dense matrix>.
#> ✔ Checking input parameters.
#> ✔ Using L0 sparse regression model.
#> ✔ Using 1 core.
#> ✔ Run done.
network_format(
network_table,
regulators = c("g1")
)
#> regulator target weight Interaction
#> 1 g1 g2 0.338137767 Activation
#> 2 g1 g18 0.290239415 Repression
#> 3 g1 g17 0.029059626 Activation
#> 4 g1 g5 0.022905010 Activation
#> 5 g1 g9 0.022804262 Repression
#> 6 g1 g4 0.019752793 Repression
#> 7 g1 g6 0.015979959 Repression
#> 8 g1 g7 0.012455804 Activation
#> 9 g1 g16 0.011136553 Repression
#> 10 g1 g15 0.009534899 Activation
#> 11 g1 g3 0.009268191 Activation
#> 12 g1 g14 0.008791775 Repression
#> 13 g1 g12 0.006908786 Activation
#> 14 g1 g10 0.005505657 Activation
#> 15 g1 g11 0.005331591 Activation
#> 16 g1 g8 0.004970697 Activation
#> 17 g1 g13 0.001101969 Repression
network_format(
network_table,
regulators = c("g1"),
abs_weight = FALSE
)
#> regulator target weight
#> 1 g1 g2 0.338137767
#> 2 g1 g18 -0.290239415
#> 3 g1 g17 0.029059626
#> 4 g1 g5 0.022905010
#> 5 g1 g9 -0.022804262
#> 6 g1 g4 -0.019752793
#> 7 g1 g6 -0.015979959
#> 8 g1 g7 0.012455804
#> 9 g1 g16 -0.011136553
#> 10 g1 g15 0.009534899
#> 11 g1 g3 0.009268191
#> 12 g1 g14 -0.008791775
#> 13 g1 g12 0.006908786
#> 14 g1 g10 0.005505657
#> 15 g1 g11 0.005331591
#> 16 g1 g8 0.004970697
#> 17 g1 g13 -0.001101969
network_format(
network_table,
targets = c("g3")
)
#> regulator target weight Interaction
#> 1 g4 g3 0.4151977047 Activation
#> 2 g2 g3 0.2814299337 Activation
#> 3 g5 g3 0.0788459511 Activation
#> 4 g12 g3 0.0286531102 Repression
#> 5 g18 g3 0.0286111327 Activation
#> 6 g17 g3 0.0268390173 Repression
#> 7 g14 g3 0.0258817747 Activation
#> 8 g7 g3 0.0216999516 Activation
#> 9 g15 g3 0.0184939819 Repression
#> 10 g10 g3 0.0160668278 Activation
#> 11 g16 g3 0.0138763419 Activation
#> 12 g13 g3 0.0131577355 Activation
#> 13 g11 g3 0.0117980066 Repression
#> 14 g1 g3 0.0092681907 Activation
#> 15 g8 g3 0.0050327240 Activation
#> 16 g9 g3 0.0048879492 Repression
#> 17 g6 g3 0.0002596662 Repression
network_format(
network_table,
regulators = c("g1", "g3"),
targets = c("g3", "g5")
)
#> regulator target weight Interaction
#> 1 g3 g5 0.062181513 Activation
#> 2 g1 g5 0.022905010 Activation
#> 3 g1 g3 0.009268191 Activation