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)[source]#

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].

Parameters:

model Literal['krumsiek11', 'toggleswitch']: Model file in ‘sim_models’ directory.
params_file bool (default: True): Read default params from file.
tmax int | None (default: None): Number of time steps per realization of time series.
branching bool | None (default: None): Only write realizations that contain new branches.
nrRealizations int | None (default: None): Number of realizations.
noiseObs float | None (default: None): Observatory/Measurement noise.
noiseDyn float | None (default: None): Dynamic noise.
step int | None (default: None): Interval for saving state of system.
seed int | None (default: None): Seed for generation of random numbers.
writedir Path | str | None (default: None): Path to directory for writing output files.

Return type:

AnnData

Returns:

Annotated data matrix.

Examples

See this use case

scanpy.tl.sim

Contents

scanpy.tl.sim#