OE_AA_matrix.f: Perturbation analysis: Overexpressions after first activation
In SPIDDOR/SPIDDOR: SPIDDOR

Description Usage Arguments Value See Also Examples

Evaluates the effect of single node overexpressions after first activation of the node on the network by computing the Perturbation index (%ON-Perturbed / %ON-Normal) of the nodes.

OE_AA_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 `read.Boolean.functions` for more details.
`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 OE_matrix.f

#Load the example network:
## Not run: 
 data(Example_network)
 #Read the Boolean functions:
 BN <- read.Boolean.functions(Lines=BN$BooleanFunctions)
 #Perturbation analysis: knockouts
 OE_AA.m<-OE_AA_matrix.f(BN,time.steps=999,repetitions=24,asynchronous=TRUE)
 #Create a heatmap of the results:
 Create_heatmap(OE_AA.m)
 
## End(Not run)