scvi.data.read_loom

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scvi.data.read_loom#

scvi.data.read_loom(filename, *, sparse=True, cleanup=False, X_name='spliced', obs_names='CellID', obsm_names=None, var_names='Gene', varm_names=None, dtype='float32', obsm_mapping=mappingproxy({}), varm_mapping=mappingproxy({}), **kwargs)[source]#

Read .loom-formatted hdf5 file.

This reads the whole file into memory.

Beware that you have to explicitly state when you want to read the file as sparse data.

Parameters:

  • filename (PathLike) – The filename.

  • sparse (bool (default: True)) – Whether to read the data matrix as sparse.

  • cleanup (bool (default: False)) – Whether to collapse all obs/var fields that only store one unique value into .uns[‘loom-.’].

  • X_name (str (default: 'spliced')) – Loompy key with which the data matrix X is initialized.

  • obs_names (str (default: 'CellID')) – Loompy key where the observation/cell names are stored.

  • obsm_mapping (Mapping[str, Iterable[str]] (default: mappingproxy({}))) – Loompy keys which will be constructed into observation matrices

  • var_names (str (default: 'Gene')) – Loompy key where the variable/gene names are stored.

  • varm_mapping (Mapping[str, Iterable[str]] (default: mappingproxy({}))) – Loompy keys which will be constructed into variable matrices

  • **kwargs – Arguments to loompy.connect

Return type:

AnnData

Example

pbmc = anndata.read_loom(
    "pbmc.loom",
    sparse=True,
    X_name="lognorm",
    obs_names="cell_names",
    var_names="gene_names",
    obsm_mapping={
        "X_umap": ["umap_1", "umap_2"]
    }
)
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