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 matrixX
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 matricesvar_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