Subset sdata by list of elements #208

SpatialData object with:
├── Images
│     ├── 'morphology_focus': MultiscaleSpatialImage[cyx] (1, 25778, 35416), (1, 12889, 17708), (1, 6444, 8854), (1, 3222, 4427), (1, 1611, 2213)
│     └── 'morphology_mip': MultiscaleSpatialImage[cyx] (1, 25778, 35416), (1, 12889, 17708), (1, 6444, 8854), (1, 3222, 4427), (1, 1611, 2213)
├── Points
│     └── 'transcripts': DataFrame with shape: (42638083, 9) (3D points)
├── Shapes
│     ├── 'cell_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)
│     ├── 'cell_circles': GeoDataFrame shape: (167780, 2) (2D shapes)
│     └── 'nucleus_boundaries': GeoDataFrame shape: (167780, 1) (2D shapes)
└── Table
      └── AnnData object with n_obs × n_vars = 167780 × 313
    obs: 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region', 'dataset', 'celltype_major', 'celltype_minor'
    var: 'gene_ids', 'feature_types', 'genome'
    uns: 'spatialdata_attrs'
    obsm: 'spatial': AnnData (167780, 313)
with coordinate systems:
▸ 'aligned', with elements:
        morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)
▸ 'global', with elements:
        morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes)

sdata.query.elements(['morphology_focus', 'cell_circles'], filter_table=True)