stlearn.spatial.trajectory.pseudotime

stlearn.spatial.trajectory.pseudotime(adata: anndata._core.anndata.AnnData, use_label: str = 'louvain', eps: float = 20, n_neighbors: int = 25, use_rep: str = 'X_pca_morphology', threshold: float = 0.01, radius: int = 50, method: str = 'mean', threshold_spots: int = 5, use_sme: bool = False, reverse: bool = False, copy: bool = False) → Optional[anndata._core.anndata.AnnData]

Perform pseudotime analysis.

Parameters

• adata – Annotated data matrix.

• use_label – Use label result of clustering method.

• eps – The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function.

• threshold – Threshold to find the significant connection for PAGA graph.

• radius – radius to adjust data for diffusion map

• method – method to adjust the data.

• use_sme – Use adjusted feature by SME normalization or not

• copy – Return a copy instead of writing to adata.

Returns

Return type

Anndata