scanpy.tl.sim

Contents

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 [Wittmann et al., 2009] [Wolf et al., 2018].

Sample from a stochastic differential equation model built from literature-curated boolean gene regulatory networks, as suggested by Wittmann et al. [2009]. The Scanpy implementation is due to Wolf et al. [2018].

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