simBoolGtn {bnem} | 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

### Value

list with the corresponding prior graph, ground truth network and data

### Author(s)

Martin Pirkl

### Examples

    sim <- simBoolGtn()
    plot(sim)

[Package bnem version 1.0.0 Index]