scanpy.read_visium

scanpy.read_visium(path, genome=None, *, count_file='filtered_feature_bc_matrix.h5', library_id=None, load_images=True, source_image_path=None)

Read 10x-Genomics-formatted visum dataset.

In addition to reading regular 10x output, this looks for the spatial folder and loads images, coordinates and scale factors. Based on the Space Ranger output docs.

See spatial() for a compatible plotting function.

Parameters:

path : Union[str, Path]

Path to directory for visium datafiles.

genome : Optional[str] (default: None)

Filter expression to genes within this genome.

count_file : str (default: 'filtered_feature_bc_matrix.h5')

Which file in the passed directory to use as the count file. Typically would be one of: ‘filtered_feature_bc_matrix.h5’ or ‘raw_feature_bc_matrix.h5’.

library_id : Optional[str] (default: None)

Identifier for the visium library. Can be modified when concatenating multiple adata objects.

source_image_path : Union[str, Path, None] (default: None)

Path to the high-resolution tissue image. Path will be included in .uns["spatial"][library_id]["metadata"]["source_image_path"].

Return type:

AnnData

Returns:

: Annotated data matrix, where observations/cells are named by their barcode and variables/genes by gene name. Stores the following information:

X

The data matrix is stored

obs_names

Cell names

var_names

Gene names

var['gene_ids']

Gene IDs

var['feature_types']

Feature types

uns['spatial']

Dict of spaceranger output files with ‘library_id’ as key

uns['spatial'][library_id]['images']

Dict of images ('hires' and 'lowres')

uns['spatial'][library_id]['scalefactors']

Scale factors for the spots

uns['spatial'][library_id]['metadata']

Files metadata: ‘chemistry_description’, ‘software_version’, ‘source_image_path’

obsm['spatial']

Spatial spot coordinates, usable as basis by embedding().