scanpy.tl.sim

scanpy.tl.sim(model, 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] [Wolf17].

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 [Wolf17].

Parameters:

model : {'krumsiek11', 'toggleswitch'}

Model file in ‘sim_models’ directory.

params_file : bool, (default: True)

Read default params from file.

tmax : int, optional (default: None)

Number of time steps per realization of time series.

branching : bool, optional (default: None)

Only write realizations that contain new branches.

nrRealizations : int, optional (default: None)

Number of realizations.

noiseObs : float, optional (default: None)

Observatory/Measurement noise.

noiseDyn : float, optional (default: None)

Dynamic noise.

step : int, optional (default: None)

Interval for saving state of system.

seed : int, optional (default: None)

Seed for generation of random numbers.

writedir : str, optional (default: None)

Path to directory for writing output files.

Returns:

adata – Annotated data matrix.

Return type:

AnnData

Examples

See this use case