dynamo.read_loom

dynamo.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) – Whether to read the data matrix as sparse.

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

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

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

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

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

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

  • **kwargs – Arguments to loompy.connect

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"]
    }
)
Return type

AnnData