troutpy.tl.distance_to_source_cell

troutpy.tl.distance_to_source_cell#

troutpy.tl.distance_to_source_cell(sdata, layer='transcripts', xcoord='x', ycoord='y', xcellcoord='x_centroid', ycellcoord='y_centroid', gene_key='gene', copy=False)#

It computes the distance from each extracellular RNA transcript to the nearest source cell based on their spatial coordinates. The function uses a KDTree to efficiently find the closest cell to each transcript, storing the results in the sdata object.

Parameters:

  • sdata (spatialdata.SpatialData) – The AnnData object containing both transcript and cellular data.

  • layer (str) – The layer in sdata containing the transcript data. Default is ‘transcripts’.

  • xcoord (str) – The column name in the transcript data for the x-coordinate. Default is ‘x’.

  • ycoord (str) – The column name in the transcript data for the y-coordinate. Default is ‘y’.

  • xcellcoord (str) – The column name in the cellular data for the x-coordinate of cell centroids. Default is ‘x_centroid’.

  • ycellcoord (str) – The column name in the cellular data for the y-coordinate of cell centroids. Default is ‘y_centroid’.

  • gene_key (str) – The column name for the gene identifier. Default is ‘feature_name’.

  • copy (str) – Whether to return a copy of the sdata object with updated distances, or modify in place. Default is False.

Returns:

AnnData or None: anndata.AnnData If copy is True, returns the updated sdata object. Otherwise, modifies sdata in place and returns None.

Notes

The function assumes that the transcript data contains a column transcript_id and that the cellular data contains cell centroids for spatial coordinates. The KDTree algorithm is used to compute the closest cell for each transcript. The resulting distances are stored in the distance_to_source_cell column of the sdata object’s transcript layer, and the closest source cell is stored in the closest_source_cell column. The median distance for each gene is also added to the xrna_metadata in the var attribute of sdata.

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