Curate and ingest spatial data¶
Now that we’ve analyzed and visualized the example dataset in the previous notebooks, let’s learn how to curate and ingest our own spatial data.
import lamindb as ln
import bionty as bt
import spatialdata as sd
ln.track()
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→ connected lamindb: testuser1/test-spatial
/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/xarray_schema/__init__.py:1: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
from pkg_resources import DistributionNotFound, get_distribution
→ created Transform('aGMYMcsFfXNC0000', key='spatial3.ipynb'), started new Run('9LpBMHogFyj7JLyJ') at 2025-12-17 19:52:23 UTC
→ notebook imports: bionty==1.10.0 lamindb==1.17.0 spatialdata==0.6.1
• recommendation: to identify the notebook across renames, pass the uid: ln.track("aGMYMcsFfXNC")
Creating artifacts¶
You can use from_spatialdata() method to create an Artifact object from a SpatialData object.
example_blobs_sdata = ln.core.datasets.spatialdata_blobs()
example_blobs_sdata
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SpatialData object
├── Images
│ ├── 'blobs_image': DataArray[cyx] (3, 512, 512)
│ └── 'blobs_multiscale_image': DataTree[cyx] (3, 512, 512), (3, 256, 256), (3, 128, 128)
├── Labels
│ ├── 'blobs_labels': DataArray[yx] (512, 512)
│ └── 'blobs_multiscale_labels': DataTree[yx] (512, 512), (256, 256), (128, 128)
├── Points
│ └── 'blobs_points': DataFrame with shape: (<Delayed>, 4) (2D points)
├── Shapes
│ ├── 'blobs_circles': GeoDataFrame shape: (5, 2) (2D shapes)
│ ├── 'blobs_multipolygons': GeoDataFrame shape: (2, 1) (2D shapes)
│ └── 'blobs_polygons': GeoDataFrame shape: (5, 1) (2D shapes)
└── Tables
└── 'table': AnnData (26, 3)
with coordinate systems:
▸ 'global', with elements:
blobs_image (Images), blobs_multiscale_image (Images), blobs_labels (Labels), blobs_multiscale_labels (Labels), blobs_points (Points), blobs_circles (Shapes), blobs_multipolygons (Shapes), blobs_polygons (Shapes)
blobs_af = ln.Artifact.from_spatialdata(
example_blobs_sdata, key="example_blobs.zarr"
).save()
blobs_af
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→ writing the in-memory object into cache
Artifact(uid='VGPsp2roREcLBm4E0000', version=None, is_latest=True, key='example_blobs.zarr', description=None, suffix='.zarr', kind='dataset', otype='SpatialData', size=13054761, hash='UYT7tNtrjsPUywOuZcOUqQ', n_files=79, n_observations=None, branch_id=1, space_id=1, storage_id=2, run_id=3, schema_id=None, created_by_id=2, created_at=2025-12-17 19:52:26 UTC, is_locked=False)
To retrieve the object back from the database you can, e.g., query by key:
example_blobs_sdata = ln.Artifact.get(key="example_blobs.zarr")
local_zarr_path = blobs_af.cache() # returns a local path to the cached .zarr store
example_blobs_sdata = (
blobs_af.load() # calls sd.read_zarr() on a locally cached .zarr store
)
To see data lineage:
blobs_af.view_lineage()
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Curating artifacts¶
For the remainder of the guide, we will work with two 10X Xenium and a 10X Visium H&E image datasets that were ingested in raw form here.
Metadata is stored in two places in the SpatialData object:
Dataset level metadata is stored in
sdata.attrs["sample"].Measurement specific metadata is stored in the associated tables in
sdata.tables.
Define a schema¶
We define a lamindb.Schema to curate both sample and table metadata.
Curating different spatial technologies
Reading different spatial technologies into SpatialData objects can result in very different objects with different metadata. Therefore, it can be useful to define technology specific Schemas by reusing Schema components.
# define features
ln.Feature(name="organism", dtype=bt.Organism).save()
ln.Feature(name="assay", dtype=bt.ExperimentalFactor).save()
ln.Feature(name="disease", dtype=bt.Disease).save()
ln.Feature(name="tissue", dtype=bt.Tissue).save()
ln.Feature(name="celltype_major", dtype=bt.CellType).save()
# define simple schemas
flexible_metadata_schema = ln.Schema(
name="Flexible metadata", itype=ln.Feature, coerce_dtype=True
).save()
ensembl_gene_ids = ln.Schema(
name="Spatial var level (Ensembl gene id)", itype=bt.Gene.ensembl_gene_id
).save()
# define composite schema
spatial_schema = ln.Schema(
name="Spatialdata schema (flexible)",
otype="SpatialData",
slots={
"attrs:sample": flexible_metadata_schema,
"tables:table:obs": flexible_metadata_schema,
"tables:table:var.T": ensembl_gene_ids,
},
).save()
Curate a Xenium dataset¶
Create the central query object of our public lamindata instance:
db = ln.DB("laminlabs/lamindata")
# load first of two cropped Xenium datasets
xenium_aligned_1_sdata = db.Artifact.get(key="xenium_aligned_1_guide_min.zarr").load()
xenium_aligned_1_sdata
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/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/lamindb/core/storage/_zarr.py:119: UserWarning: SpatialData is not stored in the most current format. If you want to use Zarr v3, please write the store to a new location using `sdata.write()`.
scverse_obj = with_package("spatialdata", lambda mod: mod.read_zarr(store))
→ transferred: Artifact(uid='kVMuYil81BHTwQ9G0001')
/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/zarr/core/group.py:3535: ZarrUserWarning: Object at zmetadata is not recognized as a component of a Zarr hierarchy.
warnings.warn(
SpatialData object, with associated Zarr store: /home/runner/.cache/lamindb/lamindata/xenium_aligned_1_guide_min.zarr
├── Images
│ ├── 'morphology_focus': DataTree[cyx] (1, 2310, 3027), (1, 1155, 1514), (1, 578, 757), (1, 288, 379), (1, 145, 189)
│ └── 'morphology_mip': DataTree[cyx] (1, 2310, 3027), (1, 1155, 1514), (1, 578, 757), (1, 288, 379), (1, 145, 189)
├── Points
│ └── 'transcripts': DataFrame with shape: (<Delayed>, 8) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (1899, 1) (2D shapes)
│ └── 'cell_circles': GeoDataFrame shape: (1812, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1812, 313)
with coordinate systems:
▸ 'aligned', with elements:
morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes)
▸ 'global', with elements:
morphology_focus (Images), morphology_mip (Images), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes)
xenium_curator = ln.curators.SpatialDataCurator(xenium_aligned_1_sdata, spatial_schema)
try:
xenium_curator.validate()
except ln.errors.ValidationError as error:
print(error)
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! 1 term not validated in feature 'columns' in slot 'attrs:sample': 'panel'
→ fix typos, remove non-existent values, or save terms via: curator.slots['attrs:sample'].cat.add_new_from('columns')
! 10 terms not validated in feature 'columns' in slot 'tables:table:obs': 'control_probe_counts', 'transcript_counts', 'control_codeword_counts', 'celltype_minor', 'dataset', 'cell_area', 'cell_id', 'total_counts', 'region', 'nucleus_area'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('columns')
! 9 terms not validated in feature 'celltype_major' in slot 'tables:table:obs': 'CAFs', 'Endothelial', 'Myeloid', 'PVL', 'T-cells', 'B-cells', 'Normal Epithelial', 'Plasmablasts', 'Cancer Epithelial'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('celltype_major')
9 terms not validated in feature 'celltype_major' in slot 'tables:table:obs': 'CAFs', 'Endothelial', 'Myeloid', 'PVL', 'T-cells', 'B-cells', 'Normal Epithelial', 'Plasmablasts', 'Cancer Epithelial'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('celltype_major')
xenium_aligned_1_sdata.tables["table"].obs["celltype_major"] = (
xenium_aligned_1_sdata.tables["table"]
.obs["celltype_major"]
.replace(
{
"CAFs": "cancer associated fibroblast",
"Endothelial": "endothelial cell",
"Myeloid": "myeloid cell",
"PVL": "perivascular cell",
"T-cells": "T cell",
"B-cells": "B cell",
"Normal Epithelial": "epithelial cell",
"Plasmablasts": "plasmablast",
"Cancer Epithelial": "neoplastic epithelial cell",
}
)
)
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/tmp/ipykernel_3006/4072479217.py:4: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.
.replace(
try:
xenium_curator.validate()
except ln.errors.ValidationError as error:
print(error)
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! 1 term not validated in feature 'columns' in slot 'attrs:sample': 'panel'
→ fix typos, remove non-existent values, or save terms via: curator.slots['attrs:sample'].cat.add_new_from('columns')
! 10 terms not validated in feature 'columns' in slot 'tables:table:obs': 'control_probe_counts', 'transcript_counts', 'control_codeword_counts', 'celltype_minor', 'dataset', 'cell_area', 'cell_id', 'total_counts', 'region', 'nucleus_area'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('columns')
! 2 terms not validated in feature 'celltype_major' in slot 'tables:table:obs': 'cancer associated fibroblast', 'neoplastic epithelial cell'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('celltype_major')
2 terms not validated in feature 'celltype_major' in slot 'tables:table:obs': 'cancer associated fibroblast', 'neoplastic epithelial cell'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('celltype_major')
xenium_curator.slots["tables:table:obs"].cat.add_new_from("celltype_major")
xenium_1_curated_af = xenium_curator.save_artifact(key="xenium1.zarr")
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! 1 term not validated in feature 'columns' in slot 'attrs:sample': 'panel'
→ fix typos, remove non-existent values, or save terms via: curator.slots['attrs:sample'].cat.add_new_from('columns')
! 10 terms not validated in feature 'columns' in slot 'tables:table:obs': 'control_probe_counts', 'transcript_counts', 'control_codeword_counts', 'celltype_minor', 'dataset', 'cell_area', 'cell_id', 'total_counts', 'region', 'nucleus_area'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('columns')
→ writing the in-memory object into cache
xenium_1_curated_af.describe()
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Artifact: xenium1.zarr (0000) ├── uid: xNkTj5sa1KiOZsiW0000 run: 9LpBMHo (spatial3.ipynb) │ kind: dataset otype: SpatialData │ hash: P76s8TuZgc5dcOKQc_R-tQ size: 34.8 MB │ branch: main space: all │ created_at: 2025-12-17 19:52:40 UTC created_by: testuser1 │ n_files: 101 ├── storage/path: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/xNkTj5sa1KiOZsiW.zarr ├── Dataset features │ ├── attrs:sample (4) │ │ assay bionty.ExperimentalFactor 10x Xenium │ │ disease bionty.Disease ductal breast carcinoma in situ │ │ organism bionty.Organism human │ │ tissue bionty.Tissue breast │ ├── tables:table:obs (1) │ │ celltype_major bionty.CellType B cell, T cell, cancer associated fibro… │ └── tables:table:var.T (313 biont… │ ABCC11 num │ ACTA2 num │ ACTG2 num │ ADAM9 num │ ADGRE5 num │ ADH1B num │ ADIPOQ num │ AGR3 num │ AHSP num │ AIF1 num │ AKR1C1 num │ AKR1C3 num │ ALDH1A3 num │ ANGPT2 num │ ANKRD28 num │ ANKRD29 num │ ANKRD30A num │ APOBEC3A num │ APOBEC3B num │ APOC1 num └── Labels └── .organisms bionty.Organism human .tissues bionty.Tissue breast .cell_types bionty.CellType endothelial cell, myeloid cell, perivas… .diseases bionty.Disease ductal breast carcinoma in situ .experimental_factors bionty.ExperimentalFactor 10x Xenium
Curate additional Xenium datasets¶
We can reuse the same curator for a second Xenium dataset:
xenium_aligned_2_sdata = db.Artifact.get(key="xenium_aligned_2_guide_min.zarr").load()
xenium_aligned_2_sdata.tables["table"].obs["celltype_major"] = (
xenium_aligned_2_sdata.tables["table"]
.obs["celltype_major"]
.replace(
{
"CAFs": "cancer associated fibroblast",
"Endothelial": "endothelial cell",
"Myeloid": "myeloid cell",
"PVL": "perivascular cell",
"T-cells": "T cell",
"B-cells": "B cell",
"Normal Epithelial": "epithelial cell",
"Plasmablasts": "plasmablast",
"Cancer Epithelial": "neoplastic epithelial cell",
}
)
)
Show code cell output
/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/lamindb/core/storage/_zarr.py:119: UserWarning: SpatialData is not stored in the most current format. If you want to use Zarr v3, please write the store to a new location using `sdata.write()`.
scverse_obj = with_package("spatialdata", lambda mod: mod.read_zarr(store))
no parent found for <ome_zarr.reader.Label object at 0x7f7837ff75f0>: None
no parent found for <ome_zarr.reader.Label object at 0x7f7837ff7710>: None
→ transferred: Artifact(uid='KFhRNPqcdoxBCNZt0001')
/tmp/ipykernel_3006/980259403.py:6: FutureWarning: The behavior of Series.replace (and DataFrame.replace) with CategoricalDtype is deprecated. In a future version, replace will only be used for cases that preserve the categories. To change the categories, use ser.cat.rename_categories instead.
.replace(
xenium_2_curated_af = ln.Artifact.from_spatialdata(
xenium_aligned_2_sdata, key="xenium2.zarr", schema=spatial_schema
).save()
Show code cell output
→ writing the in-memory object into cache
→ loading artifact into memory for validation
! 1 term not validated in feature 'columns' in slot 'attrs:sample': 'panel'
→ fix typos, remove non-existent values, or save terms via: curator.slots['attrs:sample'].cat.add_new_from('columns')
! 10 terms not validated in feature 'columns' in slot 'tables:table:obs': 'control_probe_counts', 'transcript_counts', 'control_codeword_counts', 'celltype_minor', 'dataset', 'cell_area', 'cell_id', 'total_counts', 'region', 'nucleus_area'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('columns')
→ returning schema with same hash: Schema(uid='PVaj40knBKNnn3nU', name=None, description=None, n=4, is_type=False, itype='Feature', otype=None, dtype=None, hash='hNDrqhdqN5aMV9KR2MLqjw', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=2, run_id=3, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-12-17 19:52:40 UTC, is_locked=False)
→ returning schema with same hash: Schema(uid='aKzEHeLtRUbfnhkC', name=None, description=None, n=1, is_type=False, itype='Feature', otype=None, dtype=None, hash='76eubvhExlwt0PlQ6P92lQ', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=2, run_id=3, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-12-17 19:52:41 UTC, is_locked=False)
→ returning schema with same hash: Schema(uid='2guB8oYQJYnVq3im', name=None, description=None, n=313, is_type=False, itype='bionty.Gene.ensembl_gene_id', otype=None, dtype='num', hash='FFFt-2qmlVALrsMUPNoH0g', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=2, run_id=3, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-12-17 19:52:41 UTC, is_locked=False)
xenium_2_curated_af.describe()
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Artifact: xenium2.zarr (0000) ├── uid: BMsyvZqOpJBNrDNR0000 run: 9LpBMHo (spatial3.ipynb) │ kind: dataset otype: SpatialData │ hash: qo8rAMluoKIz9pSBYknGkQ size: 36.8 MB │ branch: main space: all │ created_at: 2025-12-17 19:52:45 UTC created_by: testuser1 │ n_files: 126 ├── storage/path: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/BMsyvZqOpJBNrDNR.zarr ├── Dataset features │ ├── attrs:sample (4) │ │ assay bionty.ExperimentalFactor 10x Xenium │ │ disease bionty.Disease ductal breast carcinoma in situ │ │ organism bionty.Organism human │ │ tissue bionty.Tissue breast │ ├── tables:table:obs (1) │ │ celltype_major bionty.CellType B cell, T cell, cancer associated fibro… │ └── tables:table:var.T (313 biont… │ ABCC11 num │ ACTA2 num │ ACTG2 num │ ADAM9 num │ ADGRE5 num │ ADH1B num │ ADIPOQ num │ AGR3 num │ AHSP num │ AIF1 num │ AKR1C1 num │ AKR1C3 num │ ALDH1A3 num │ ANGPT2 num │ ANKRD28 num │ ANKRD29 num │ ANKRD30A num │ APOBEC3A num │ APOBEC3B num │ APOC1 num └── Labels └── .organisms bionty.Organism human .tissues bionty.Tissue breast .cell_types bionty.CellType endothelial cell, myeloid cell, perivas… .diseases bionty.Disease ductal breast carcinoma in situ .experimental_factors bionty.ExperimentalFactor 10x Xenium
Curate Visium datasets¶
Analogously, we can define a Schema and Curator for Visium datasets:
visium_aligned_sdata = db.Artifact.get(key="visium_aligned_guide_min.zarr").load()
visium_aligned_sdata
Show code cell output
/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/lamindb/core/storage/_zarr.py:119: UserWarning: SpatialData is not stored in the most current format. If you want to use Zarr v3, please write the store to a new location using `sdata.write()`.
scverse_obj = with_package("spatialdata", lambda mod: mod.read_zarr(store))
→ transferred: Artifact(uid='bjH534dxVi1drmLZ0001')
/opt/hostedtoolcache/Python/3.12.12/x64/lib/python3.12/site-packages/zarr/core/group.py:3535: ZarrUserWarning: Object at zmetadata is not recognized as a component of a Zarr hierarchy.
warnings.warn(
SpatialData object, with associated Zarr store: /home/runner/.cache/lamindb/lamindata/visium_aligned_guide_min.zarr
├── Images
│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_full_image': DataTree[cyx] (3, 1213, 952), (3, 607, 476), (3, 303, 238), (3, 152, 119), (3, 76, 60)
│ ├── 'CytAssist_FFPE_Human_Breast_Cancer_hires_image': DataArray[cyx] (3, 113, 88)
│ └── 'CytAssist_FFPE_Human_Breast_Cancer_lowres_image': DataArray[cyx] (3, 34, 27)
├── Shapes
│ └── 'CytAssist_FFPE_Human_Breast_Cancer': GeoDataFrame shape: (37, 2) (2D shapes)
└── Tables
└── 'table': AnnData (37, 18085)
with coordinate systems:
▸ 'aligned', with elements:
CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)
▸ 'downscaled_hires', with elements:
CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)
▸ 'downscaled_lowres', with elements:
CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)
▸ 'global', with elements:
CytAssist_FFPE_Human_Breast_Cancer_full_image (Images), CytAssist_FFPE_Human_Breast_Cancer_hires_image (Images), CytAssist_FFPE_Human_Breast_Cancer_lowres_image (Images), CytAssist_FFPE_Human_Breast_Cancer (Shapes)
visium_curated_af = ln.Artifact.from_spatialdata(
visium_aligned_sdata, key="visium.zarr", schema=spatial_schema
).save()
Show code cell output
→ writing the in-memory object into cache
→ loading artifact into memory for validation
! 7 terms not validated in feature 'columns' in slot 'tables:table:obs': 'spot_id', 'dataset', 'in_tissue', 'clone', 'array_col', 'region', 'array_row'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:obs'].cat.add_new_from('columns')
! no values were validated for columns!
→ starting creation of 17761 Gene records in batches of 10000
! 17 terms not validated in feature 'columns' in slot 'tables:table:var.T': 'ENSG00000284824', 'ENSG00000240224', 'ENSG00000243135', 'ENSG00000112096', 'ENSG00000285162', 'ENSG00000183729', 'ENSG00000285447', 'ENSG00000130723', 'ENSG00000274897', 'ENSG00000215271', 'ENSG00000221995', 'ENSG00000183791', 'ENSG00000263264', 'ENSG00000182584', 'ENSG00000184258', 'ENSG00000277203', 'ENSG00000286265'
→ fix typos, remove non-existent values, or save terms via: curator.slots['tables:table:var.T'].cat.add_new_from('columns')
→ returning schema with same hash: Schema(uid='PVaj40knBKNnn3nU', name=None, description=None, n=4, is_type=False, itype='Feature', otype=None, dtype=None, hash='hNDrqhdqN5aMV9KR2MLqjw', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=2, run_id=3, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-12-17 19:52:40 UTC, is_locked=False)
→ returning schema with same hash: Schema(uid='RnZuIefNHdqVcu2R', name='Flexible metadata', description=None, is_type=False, itype='Feature', otype=None, dtype=None, hash='jKTX5yzmVwIdJdHH2ZfMAA', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=2, run_id=3, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-12-17 19:52:27 UTC, is_locked=False)
→ not annotating with 18068 features for slot tables:table:var.T as it exceeds 1000 (ln.settings.annotation.n_max_records)
visium_curated_af.describe()
Show code cell output
Artifact: visium.zarr (0000) ├── uid: AmmYOyTfDdUnrrly0000 run: 9LpBMHo (spatial3.ipynb) │ kind: dataset otype: SpatialData │ hash: 0HOmjAWXse6k_bwavGFN7Q size: 4.4 MB │ branch: main space: all │ created_at: 2025-12-17 19:52:56 UTC created_by: testuser1 │ n_files: 91 ├── storage/path: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/AmmYOyTfDdUnrrly.zarr ├── Dataset features │ ├── attrs:sample (4) │ │ assay bionty.ExperimentalFactor Visium Spatial Gene Expression │ │ disease bionty.Disease ductal breast carcinoma in situ │ │ organism bionty.Organism human │ │ tissue bionty.Tissue breast │ ├── tables:table:obs (-1) │ └── tables:table:var.T (18068 bio… └── Labels └── .organisms bionty.Organism human .tissues bionty.Tissue breast .diseases bionty.Disease ductal breast carcinoma in situ .experimental_factors bionty.ExperimentalFactor Visium Spatial Gene Expression
Overview of the curated datasets¶
visium_curated_af.view_lineage()
ln.Artifact.to_dataframe(features=True, include=["hash", "size"])
Show code cell output
→ queried for all categorical features of dtypes Record or ULabel and non-categorical features: (0) []
| uid | key | hash | size | |
|---|---|---|---|---|
| id | ||||
| 10 | AmmYOyTfDdUnrrly0000 | visium.zarr | 0HOmjAWXse6k_bwavGFN7Q | 4650604 |
| 9 | bjH534dxVi1drmLZ0001 | visium_aligned_guide_min.zarr | a8rVkf_kjp9To9KI06i03g | 5809684 |
| 8 | BMsyvZqOpJBNrDNR0000 | xenium2.zarr | qo8rAMluoKIz9pSBYknGkQ | 38639497 |
| 7 | KFhRNPqcdoxBCNZt0001 | xenium_aligned_2_guide_min.zarr | oH569Lh4koYRB1I6AatnGQ | 40822308 |
| 6 | xNkTj5sa1KiOZsiW0000 | xenium1.zarr | P76s8TuZgc5dcOKQc_R-tQ | 36540950 |
| 5 | kVMuYil81BHTwQ9G0001 | xenium_aligned_1_guide_min.zarr | 8f1qC6IkpSvFw2H8TdhplQ | 35115305 |
| 4 | VGPsp2roREcLBm4E0000 | example_blobs.zarr | UYT7tNtrjsPUywOuZcOUqQ | 13054761 |
| 1 | 8sPWscz3SICG1D8t0001 | xenium/2.0.0/Xenium_V1_humanLung_Cancer_FFPE_o... | jgalhtHw00CzuZA_jrTygw | 7045222972 |
ln.finish()
Show code cell output
→ finished Run('9LpBMHogFyj7JLyJ') after 33s at 2025-12-17 19:52:57 UTC