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.

  • 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.


  • 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().