, 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’}Literal[‘krumsiek11’, ‘toggleswitch’]

Model file in ‘sim_models’ directory.

params_file : boolbool (default: True)

Read default params from file.

tmax : int | NoneOptional[int] (default: None)

Number of time steps per realization of time series.

branching : bool | NoneOptional[bool] (default: None)

Only write realizations that contain new branches.

nrRealizations : int | NoneOptional[int] (default: None)

Number of realizations.

noiseObs : float | NoneOptional[float] (default: None)

Observatory/Measurement noise.

noiseDyn : float | NoneOptional[float] (default: None)

Dynamic noise.

step : int | NoneOptional[int] (default: None)

Interval for saving state of system.

seed : int | NoneOptional[int] (default: None)

Seed for generation of random numbers.

writedir : str | Path | NoneUnion[str, Path, None] (default: None)

Path to directory for writing output files.

Return type



Annotated data matrix.


See this use case