Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ---- message=FALSE, warning=FALSE, results = "hide", eval=FALSE--------------
# library(devtools)
# devtools::install_github("mbraccini/diffeRenTES")
## ----setup--------------------------------------------------------------------
library(diffeRenTES)
library(BoolNet)
## ---- message=FALSE, warning=FALSE, results = "hide",echo=FALSE---------------
set.seed(333)
## ---- warning=FALSE-----------------------------------------------------------
net <- BoolNet::generateRandomNKNetwork(10, 2)
attractors <- BoolNet::getAttractors(net)
# Attractors Transition Matrix computation
ATM <- getATM(net, attractors, MAX_STEPS_TO_FIND_ATTRACTORS = 100)
# ATM structure in matrix format.
# a1, a2, etc. refer to the attractors found.
print(ATM$ATM)
# No. perturbations that have not reach another attractor within the provided MAX_STEPS_TO_FIND_ATTRACTORS
print(ATM$lostFLips)
## ---- warning=FALSE-----------------------------------------------------------
#TESs computation
TESs <- getTESs(ATM)
#Retrieve the computed TESs
print(TESs$TES)
#And the noise thresholds at which they emerge.
print(TESs$thresholds)
## ----message=FALSE, warning=FALSE,results = "hide"----------------------------
# Saving the TES-based differentiation tree into a file
saveDifferentiationTreeToFile(TESs, file.path(tempdir(), "example.svg"))
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