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) – 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
dtype (str) –
- Return type:
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"] } )