simBoolGtn: Sample random network and simulate data
In MartinFXP/bnem: Training of logical models from indirect measurements of perturbation experiments

simBoolGtn

R Documentation

Sample random network and simulate data

Description

Draws a random prior network, samples a ground truth from the full boolean extension and generates data

Usage

simBoolGtn(
  Sgenes = 10,
  maxEdges = 25,
  stimGenes = 2,
  layer = 1,
  frac = 0.1,
  maxInDeg = 2,
  dag = TRUE,
  maxSize = 2,
  maxStim = 2,
  maxInhibit = 1,
  Egenes = 10,
  flip = 0.33,
  reps = 1,
  keepsif = FALSE,
  negation = 0.25,
  allstim = FALSE,
  and = 0.25,
  positive = TRUE,
  verbose = FALSE
)

Arguments

`Sgenes`	number of S-genes
`maxEdges`	number of maximum edges (upper limit) in the DAG
`stimGenes`	number of stimulated S-genes
`layer`	scaling factor for the sampling of next Sgene layerof the prior. high (5-10) mean more depth and low (0-2) means more breadth
`frac`	fraction of hyper-edges in the ground truth (GTN)
`maxInDeg`	maximum number of incoming hyper-edges
`dag`	if TRUE, graph will be acyclic
`maxSize`	maximum number of S-genes in a hyper-edge
`maxStim`	maximum of stimulated S-genes in an experiment (=data samples)
`maxInhibit`	maximum number of inhibited S-genes in an experiment (=data samples)
`Egenes`	number of E-genes per S-gene, e.g. 10 S-genes and 10 E-genes will return 100 E-genes overall
`flip`	fraction of inhibited E-genes
`reps`	number of replicates
`keepsif`	if TRUE does not delete sif file, which encodes the prior network
`negation`	sample probability for negative or NOT edges
`allstim`	full network in which all S-genes are also stimulated
`and`	probability for AND-gates in the GTN
`positive`	if TRUE, sets all stimulation edges to activation, else samples inhibitory edges by 'negation' probability
`verbose`	TRUE for verbose output