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 {‘krumsiek11’, ‘toggleswitch’}
Literal[‘krumsiek11’, ‘toggleswitch’] Model file in ‘sim_models’ directory.
- params_file
bool(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