, params_file=True, tmax=None, branching=None, nrRealizations=None, noiseObs=None, noiseDyn=None, step=None, seed=None, writedir=None)

Simulate dynamic gene expression data [Wittmann09] [Wolf18].

Sample from a stochastic differential equation model built from literature-curated boolean gene regulatory networks, as suggested by [Wittmann09]. The Scanpy implementation is due to [Wolf18].

model : {'krumsiek11', 'toggleswitch'}

Model file in ‘sim_models’ directory.

params_file : boolbool

Read default params from file.

tmax : int, NoneOptional[int]

Number of time steps per realization of time series.

branching : bool, NoneOptional[bool]

Only write realizations that contain new branches.

nrRealizations : int, NoneOptional[int]

Number of realizations.

noiseObs : float, NoneOptional[float]

Observatory/Measurement noise.

noiseDyn : float, NoneOptional[float]

Dynamic noise.

step : int, NoneOptional[int]

Interval for saving state of system.

seed : int, NoneOptional[int]

Seed for generation of random numbers.

writedir : str, Path, NoneUnion[str, Path, None]

Path to directory for writing output files.

Return type



Annotated data matrix.


See this use case