simBoolGtn {bnem} | R Documentation |
Draws a random prior network, samples a ground truth from the full boolean extension and generates data
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 )
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 |
list with the corresponding prior graph, ground truth network and data
Martin Pirkl
sim <- simBoolGtn() plot(sim)