omicverse.io.spatial.read_visium

omicverse.io.spatial.read_visium(path, genome=None, *, count_file='filtered_feature_bc_matrix.h5', library_id=None, load_images=True, source_image_path=None, hires_image_path='spatial/tissue_hires_image.png', lowres_image_path='spatial/tissue_lowres_image.png', scalefactors_path='spatial/scalefactors_json.json')[source]

Read 10x-Genomics-formatted Visium 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.

Parameters:

  • path (Union[str, PathLike]) – Path to the Space Ranger output directory (typically outs/).

  • genome (Optional[str] (default: None)) – Filter expression to genes within this genome.

  • count_file (str (default: 'filtered_feature_bc_matrix.h5')) – Count matrix filename inside path. Typically 'filtered_feature_bc_matrix.h5' or 'raw_feature_bc_matrix.h5'.

  • library_id (Optional[str] (default: None)) – Identifier stored under adata.uns['spatial']. Inferred from the HDF5 library_ids attribute when not provided.

  • load_images (bool (default: True)) – Whether to load tissue images, scale factors and spatial coordinates.

  • source_image_path (Union[str, PathLike, None] (default: None)) – Optional path to the full-resolution source image. Stored in adata.uns['spatial'][library_id]['metadata']['source_image_path'].

  • hires_image_path (str (default: 'spatial/tissue_hires_image.png')) – Relative path to the hires tissue image inside path.

  • lowres_image_path (str (default: 'spatial/tissue_lowres_image.png')) – Relative path to the lowres tissue image inside path.

  • scalefactors_path (str (default: 'spatial/scalefactors_json.json')) – Relative path to the scalefactors JSON inside path.

Returns:

Annotated data matrix where observations/cells are named by their

barcode and variables/genes by gene name.

  • X – count matrix

  • obs_names – barcode names

  • var_names – gene/probe names

  • var[‘gene_ids’] – gene IDs

  • var[‘feature_types’] – feature types

  • uns[‘spatial’][library_id][‘images’] – {'hires': ndarray, 'lowres': ndarray}

  • uns[‘spatial’][library_id][‘scalefactors’] – parsed scalefactors JSON

  • uns[‘spatial’][library_id][‘metadata’] – chemistry/version info

  • obsm[‘spatial’] – spot pixel coordinates (row, col in full-res image)

Return type:

adata

Examples

>>> import omicverse as ov
>>> adata = ov.io.spatial.read_visium("outs/")
>>> adata = ov.io.spatial.read_visium("outs/", count_file="raw_feature_bc_matrix.h5")