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 :
boolbool Read default params from file.
- tmax :
int,NoneOptional[int] Number of time steps per realization of time series.
- branching :
bool,NoneOptional[bool] Only write realizations that contain new branches.
- nrRealizations :
int,NoneOptional[int] Number of realizations.
- noiseObs :
float,NoneOptional[float] Observatory/Measurement noise.
- noiseDyn :
float,NoneOptional[float] Dynamic noise.
- step :
int,NoneOptional[int] Interval for saving state of system.
- seed :
int,NoneOptional[int] Seed for generation of random numbers.
- writedir :
str,Path,NoneUnion[str,Path,None] Path to directory for writing output files.
- model : {
- Return type
- Returns
Annotated data matrix.
Examples
See this use case