scanpy.tl.sim¶
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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] [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 : {‘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.
- model : {‘krumsiek11’, ‘toggleswitch’}
- Return type
- Returns
Annotated data matrix.
Examples
See this use case