stlearn.spatial.clustering.localization

stlearn.spatial.clustering.localization(adata: anndata._core.anndata.AnnData, use_label: str = 'louvain', eps: int = 20, min_samples: int = 0, copy: bool = False) → Optional[anndata._core.anndata.AnnData]

Perform local clustering by using DBSCAN.

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.

• min_samples – The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.

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

Returns

Return type

Anndata