scanpy.read_loom#
- scanpy.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
- filename
- Return type:
Example
pbmc = anndata.io.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"] } )