plot.bnem {bnem} | R Documentation |
plots the boolen network as disjunctive normal form
## S3 method for class 'bnem' plot(x, ...)
x |
bnemsim object |
... |
further arguments; see function mnem::plotDnf |
plot of boolean network
Martin Pirkl
sifMatrix <- rbind(c("A", 1, "B"), c("A", 1, "C"), c("B", 1, "D"), c("C", 1, "D")) temp.file <- tempfile(pattern="interaction",fileext=".sif") write.table(sifMatrix, file = temp.file, sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE) PKN <- CellNOptR::readSIF(temp.file) CNOlist <- dummyCNOlist("A", c("B","C","D"), maxStim = 1, maxInhibit = 2, signals = c("A", "B","C","D")) model <- CellNOptR::preprocessing(CNOlist, PKN, maxInputsPerGate = 100) expression <- matrix(rnorm(nrow(slot(CNOlist, "cues"))*10), 10, nrow(slot(CNOlist, "cues"))) fc <- computeFc(CNOlist, expression) initBstring <- rep(0, length(model$reacID)) res <- bnem(search = "greedy", model = model, CNOlist = CNOlist, fc = fc, pkn = PKN, stimuli = "A", inhibitors = c("B","C","D"), parallel = NULL, initBstring = initBstring, draw = FALSE, verbose = FALSE, maxSteps = Inf, seeds = 10) plot(res)