scvelo.read_loom¶
-
scvelo.
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)¶ 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