squidpy.tl.var_by_distance

squidpy.tl.var_by_distance(adata, groups, cluster_key=None, library_key=None, library_id=None, design_matrix_key='design_matrix', covariates=None, metric='euclidean', spatial_key='spatial', copy=False)

Build a design matrix consisting of distance measurements to selected anchor point(s) for each observation.

Parameters:

• adata (AnnData) – Annotated data object.

• groups (str | list[str] | ndarray[tuple[Any, ...], dtype[Any]]) – Anchor point(s) to calculate distances to. Can be a single or multiple observations as long as they are annotated in an .obs column with name given by cluster_key. Alternatively, a numpy array of coordinates can be passed.

• cluster_key (str | None) – Name of annotation column in .obs where the observation used as anchor points are located.

• library_key (str | None) – If multiple library_id, column in anndata.AnnData.obs which stores mapping between library_id and obs.

• library_id (str | list[str] | None) – Name of the Z-dimension(s) that this function should be applied to. For not specified Z-dimensions, the identity function is applied.

• design_matrix_key (str) – Name of the design matrix saved to .obsm.

• covariates (str | list[str] | None) – Additional covariates from .obs to include in the design matrix.

• metric (str) – Distance metric, defaults to “euclidean”.

• spatial_key (str) – Key in anndata.AnnData.obsm where spatial coordinates are stored.

• copy (bool) – If True, return the result, otherwise save it to the adata object.

Return type:

AnnData

Returns:

If copy = True, returns the design_matrix with the distances to an anchor point Otherwise, stores design_matrix in .obsm.