plot.bnembs: Plot Bootstrap result

In MartinFXP/B-NEM: Training of logical models from indirect measurements of perturbation experiments

Plot Bootstrap result

Description

Shows the result of a Boostrap with either edge frequencies or confidence intervals

Usage

## S3 method for class 'bnemBs'
plot(
  x,
  scale = 3,
  shift = 0.1,
  cut = 0.5,
  dec = 2,
  ci = 0,
  cip = 0.95,
  method = "exact",
  ...
)

Arguments

x	bnemBs object
scale	numeric value for scaling the edgewidth
shift	numeric value for shifting the edgewidth
cut	shows only edges with a fraction larger than cut
dec	integer for function round
ci	if TRUE shows confidence intervals
cip	range for the confidence interval, e.g. 0.95
method	method to use for conidence interval computation (see function binom.confint from package binom)
...	additional parameters for the function mnem::plotDnf

Value

plot of the network from the bootstrap

Author(s)

Martin Pirkl

Examples

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 <- bnemBs(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)

MartinFXP/B-NEM documentation built on Oct. 27, 2023, 8:24 p.m.