simBoolGtn: Sample random network and simulate data

Description Usage Arguments Value Author(s) Examples

View source: R/bnem_main.r

Description

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

Usage

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simBoolGtn(
  Sgenes = 10,
  maxEdges = 25,
  stimGenes = 2,
  layer = 1,
  frac = 0.1,
  dag = TRUE,
  maxSize = 2,
  maxStim = 2,
  maxInhibit = 1,
  Egenes = 10,
  flip = 0.33,
  reps = 3,
  keepsif = FALSE,
  maxcount = 10,
  negation = 0.25,
  allstim = FALSE,
  and = 0.25,
  verbose = FALSE
)

Arguments

Sgenes

number of S-genes

maxEdges

number of maximum edges in the DAG

stimGenes

number of stimulated S-genes

layer

scaling factor for the sampling of next Sgene layer; high means a higher probability for less Sgenes and low more Sgenes

frac

fraction of hyper-edges in the ground truth

dag

if TRUE graph will be acyclic

maxSize

maximum number of S-genes in a disjunction or clause

maxStim

maximum of stimulated S-genes in the data samples

maxInhibit

maximum of inhibited S-genes in the 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

number of inhibited E-genes

reps

number of replicates

keepsif

if TRUE does not delete sif file, which encodes the prior network

maxcount

while loopes ensure a reasonable network, maxcount makes sure while loops do not run to infinity

negation

has to be greater or equal to 0 and less than 1; if greater than 0, negation is the sample probability for negative edges

allstim

full network in which all S-genes are possibly stimulated

and

probability for and gates in the GTN

verbose

TRUE for verbose output

Value

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

Author(s)

Martin Pirkl

Examples

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sim <- simBoolGtn()
plot(sim)

MartinFXP/B-NEM documentation built on Aug. 30, 2020, 8:22 a.m.