stlearn.Read10X

stlearn.Read10X(path: Union[str, Path], genome: Optional[str] = None, count_file: str = 'filtered_feature_bc_matrix.h5', library_id: Optional[str] = None, load_images: Optional[bool] = True, quality: Literal['fulres', 'hires', 'lowres'] = 'hires', image_path: Optional[Union[str, Path]] = None) → AnnData[source]

Read Visium data from 10X (wrap read_visium from scanpy)

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.

Parameters:

  • path – Path to directory for visium datafiles.

  • genome – Filter expression to genes within this genome.

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

  • load_images – Load image or not.

  • quality – Set quality that convert to stlearn to use. Store in anndata.obs[‘imagecol’ & ‘imagerow’]

  • image_path – Path to image. Only need when loading full resolution image.

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 (‘fulres’, ‘hires’ and ‘lowres’)

  • uns[‘spatial’][library_id][‘scalefactors’] – Scale factors for the spots

  • uns[‘spatial’][library_id][‘metadata’] – Files metadata: ‘chemistry_description’, ‘software_version’

  • obsm[‘spatial’] – Spatial spot coordinates, usable as basis by embedding().