moscot.datasets.simulate_data#
- moscot.datasets.simulate_data(n_distributions=2, cells_per_distribution=20, n_genes=60, key='batch', var=1.0, obs_to_add=mappingproxy({'celltype': 3}), marginals=None, seed=0, quad_term=None, lin_cost_matrix=None, quad_cost_matrix=None, **kwargs)[source]#
Simulate data.
This function is used to generate data, mainly for the purpose of demonstrating certain functionalities of
moscot.- Parameters:
n_distributions (
int) – Number of distributions defined by key.cells_per_distribution (
int) – Number of cells per distribution.n_genes (
int) – Number of genes per simulated cell.key (
Literal['day','batch']) – Key to identify distribution allocation.var (
float) – Variance of one cell distributionobs_to_add (
Mapping[str,Any]) – Dictionary of names to add to columns ofanndata.AnnData.obsand number of different values for this column.marginals (
Optional[Tuple[str,str]]) – Column names ofanndata.AnnData.obswhere to save the randomly generated marginals. If None, no marginals are generated.seed (
int) – Random seed.quad_term (
Optional[Literal['tree','barcode','spatial']]) – Literal indicating whether to add costs corresponding to a specific problem setting. If None, no quadratic cost element is generated.lin_cost_matrix (
Optional[str]) – Key where to save the linear cost matrix. It is generated according to the pairwise policy. If None, no linear cost matrix is generated.quad_cost_matrix (
Optional[str]) – Key where to save the quadratic cost matrices. If None, no quadratic cost matrix is generated.kwargs (Any) –
- Return type:
- Returns: