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'}
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.
- Return type
- Returns
Annotated data matrix.
Examples
See this use case