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', **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.

  • 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_names (Optional[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.

  • obsm_names – Loompy keys which will be constructed into variable matrices

  • **kwargs – Arguments to loompy.connect

Return type