The function draws expression data from a multivariate normal distribution with block structured co-variance matrix. First, data is drawn for a control condition (no active regulators). Then data is generated for the situation that a certain fraction of regulators is turned 'on' (treatment condition). Regulator activity states are sampled from a Bernoulli distribution.

1 2 | ```
simulateData(affinities, nrep = 5, miRNAExpressions = TRUE, fn.targets = 0.1,
fp.targets = 0.2, exp.nTF = 5, exp.nmiR = 5, exp.interact = 5)
``` |

`affinities` |
regulator-target gene network (see |

`nrep` |
number of replicates per condition |

`miRNAExpressions` |
Should miRNA expression data be simulated? |

`fn.targets` |
fraction of false negative target predictions (i.e. missing edges per regulator in the bipartite regulator-gene graph) |

`fp.targets` |
fraction of false positive target predictions |

`exp.nTF` |
expected number of active TFs |

`exp.nmiR` |
expected number of active miRNAs |

`exp.interact` |
expected number of active interaction terms |

If active interaction terms should be simulated, a set of possible interaction terms has to be defined in `affinities$other`

.

`dat.mRNA ` |
mRNA data – active regulators are expected to induce a log FC of 1 |

`dat.miRNA ` |
miRNA data – active miRNAs are expected to show a log FC of 1 |

`dat.TF` |
TF expression data – active miRNAs are expected to show a log FC of 0.5 |

`miRNAstates` |
simulated miRNA activities in treatment condition |

`TFstates` |
simulated TF activities in treatment condition |

`inter.states` |
simulated regulator interaction activities in treatment condition |

Holger Froehlich

1 2 | ```
data(humanNetworkSimul)
sim = simulateData(affinities2)
``` |

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