Description Usage Arguments Value See Also Examples
Evaluates the effect of single node overexpressions on the network by computing the Perturbation index (%ON-Perturbed / %ON-Normal) of the nodes.
1 2 3 4 | OE_matrix.f(BN,time.steps=999,
Knockouts="",Over_expr="",Over_expr_AA="",
KO_times=NULL, OE_times=NULL,
asynchronous=TRUE,repetitions)
|
BN |
A list containing the most relevant information of the loaded network. See |
time.steps |
A number specifying the iterations for the simulation algorithm. We recommend to use the default value of 999 (1000 time steps in total, including the initial conditions). |
Knockouts |
A character vector with the name of the nodes to knockout (fixed to 0) over all the simulation (empty by default). |
Over_expr |
A character vector with the name of the nodes to overexpress (fixed to 1) over all the simulation (empty by default). |
Over_expr_AA |
A character vector with the name of the nodes to overexpress (fixed to 1) after their first activation (empty by default). |
KO_times |
A numeric vector specifying the iterations where the nodes in Knockouts argument will be fixed to 0. If empty the knockout is applied to the nodes for the entire simulation (empty by default). |
OE_times |
A numeric vector specifying the iterations where the nodes in Over_expr argument will be fixed to 1. If empty the overexpression is applied to the nodes for the entire simulation (empty by default). |
asynchronous |
logical; if FALSE the synchronous mode is selected; if TRUE the asynchronous scheme is chosen (by default). |
repetitions |
Number of repetitions to re-compute the simulation algorithm in order to get the attractor. We recommend first to try with a number smaller that 20. More than 100 repetitions are not allowed. |
A square matrix indicating how the overexpression of the "column node"" affects each "row node".
See Also Create_heatmap
KO_matrix.f
1 2 3 4 5 6 7 8 9 10 11 | #Load the example network:
## Not run:
data(Example_network)
#Read the Boolean functions:
BN <- read.Boolean.functions(Lines=BN$BooleanFunctions)
#Perturbation analysis: knockouts
OE.m<-OE_matrix.f(BN,time.steps=999,repetitions=24,asynchronous=TRUE)
#Create a heatmap of the results:
Create_heatmap(OE.m)
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.