anndata.experimental.sparse_dataset#
- anndata.experimental.sparse_dataset(group)[source]#
Generates a backed mode-compatible sparse dataset class.
- Parameters:
- Return type:
- Returns:
Sparse dataset class.
Example
First we’ll need a stored dataset:
>>> import scanpy as sc >>> import h5py >>> from anndata.experimental import sparse_dataset, read_elem >>> sc.datasets.pbmc68k_reduced().raw.to_adata().write_h5ad("pbmc.h5ad")
Initialize a sparse dataset from storage
>>> f = h5py.File("pbmc.h5ad") >>> X = sparse_dataset(f["X"]) >>> X CSRDataset: backend hdf5, shape (700, 765), data_dtype float32
Indexing returns sparse matrices
>>> X[100:200] <...sparse matrix of...float32...with 25003 stored elements...>
These can also be used inside of an AnnData object, no need for backed mode
>>> from anndata import AnnData >>> adata = AnnData( ... layers={"backed": X}, obs=read_elem(f["obs"]), var=read_elem(f["var"]) ... ) >>> adata.layers["backed"] CSRDataset: backend hdf5, shape (700, 765), data_dtype float32
Indexing access (i.e., from views) brings selection into memory
>>> adata[adata.obs["bulk_labels"] == "CD56+ NK"].layers[ ... "backed" ... ] <...sparse matrix of...float32...with 7340 stored elements...>