Spatial¶
Here, you’ll learn how to manage spatial datasets:
curate and ingest spatial data (
)
query & analyze spatial datasets (
)
load the collection into memory & train a ML model (
)
create and share interactive visualizations with vitessce (
)
Spatial omics data integrates molecular profiling (e.g., transcriptomics, proteomics) with spatial information, preserving the spatial organization of cells and tissues. It enables high-resolution mapping of molecular activity within biological contexts, crucial for understanding cellular interactions and microenvironments.
Many different spatial technologies such as multiplexed imaging, spatial transcriptomics, spatial proteomics, whole-slide imaging, spatial metabolomics, and 3D tissue reconstruction exist which can all be stored in the SpatialData data framework. For more details we refer to the original publication:
Marconato, L., Palla, G., Yamauchi, K.A. et al. SpatialData: an open and universal data framework for spatial omics. Nat Methods 22, 58–62 (2025). https://doi.org/10.1038/s41592-024-02212-x
Note
A collection of curated spatial datasets in SpatialData format is available on the scverse/spatialdata-db instance.
spatial data vs SpatialData terminology
When we mention spatial data, we refer to data from spatial assays, such as spatial transcriptomics or proteomics, that includes spatial coordinates to represent the organization of molecular features in tissue. When we refer SpatialData, we mean spatial omics data stored in the scverse SpatialData framework.
# pip install 'lamindb[jupyter,bionty]' spatialdata spatialdata-plot
!lamin init --storage ./test-spatial --modules bionty
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→ initialized lamindb: testuser1/test-spatial
import lamindb as ln
import bionty as bt
import spatialdata as sd
import warnings
warnings.filterwarnings("ignore")
spatial_guide_datasets = ln.Project(name="spatial guide datasets").save()
ln.track(project=spatial_guide_datasets)
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→ connected lamindb: testuser1/test-spatial
/opt/hostedtoolcache/Python/3.12.11/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('T1OMY3rtuw2z0000', key='spatial.ipynb'), started new Run('BQdBaxymLEK5qCV1') at 2025-10-30 18:59:14 UTC
→ notebook imports: bionty==1.8.1 lamindb==1.15a1 spatialdata==0.5.0
• recommendation: to identify the notebook across renames, pass the uid: ln.track("T1OMY3rtuw2z", project="spatial guide datasets")
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
INFO The Zarr backing store has been changed from None the new file path:
/home/runner/.cache/lamindb/cyXf0Vym1gBwRMIr0000.zarr
Artifact(uid='cyXf0Vym1gBwRMIr0000', version=None, is_latest=True, key='example_blobs.zarr', description=None, suffix='.zarr', kind='dataset', otype='SpatialData', size=12122461, hash='LZ9HLHkw8HhoOzS7Vt_VSg', n_files=116, n_observations=None, branch_id=1, space_id=1, storage_id=1, run_id=1, schema_id=None, created_by_id=1, created_at=2025-10-30 18:59:16 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, nullable=True).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¶
# load first of two cropped Xenium datasets
xenium_aligned_1_sdata = (
ln.Artifact.using("laminlabs/lamindata")
.get(key="xenium_aligned_1_guide_min.zarr")
.load()
)
xenium_aligned_1_sdata
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→ transferred: Artifact(uid='kVMuYil81BHTwQ9G0001'), Storage(uid='D9BilDV2')
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': 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region', 'dataset', 'celltype_minor'
→ 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",
}
)
)
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': 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region', 'dataset', 'celltype_minor'
→ 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': 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region', 'dataset', 'celltype_minor'
→ 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
INFO The SpatialData object is not self-contained (i.e. it contains some elements that are Dask-backed from
locations outside /home/runner/.cache/lamindb/3AYeC9diJJliwPdS0000.zarr). Please see the documentation of
`is_self_contained()` to understand the implications of working with SpatialData objects that are not
self-contained.
INFO The Zarr backing store has been changed from
/home/runner/.cache/lamindb/lamindata/xenium_aligned_1_guide_min.zarr the new file path:
/home/runner/.cache/lamindb/3AYeC9diJJliwPdS0000.zarr
xenium_1_curated_af.describe()
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Artifact: xenium1.zarr (0000) ├── uid: 3AYeC9diJJliwPdS0000 run: BQdBaxy (spatial.ipynb) │ kind: dataset otype: SpatialData │ hash: 68_dzfaDidoKacs0glIGNg size: 33.5 MB │ branch: main space: all │ created_at: 2025-10-30 18:59:35 UTC created_by: testuser1 │ n_files: 148 ├── storage/path: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/3AYeC9diJJliwPdS.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 └── .projects Project spatial guide datasets .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 = (
ln.Artifact.using("laminlabs/lamindata")
.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",
}
)
)
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→ transferred: Artifact(uid='KFhRNPqcdoxBCNZt0001')
xenium_2_curated_af = ln.Artifact.from_spatialdata(
xenium_aligned_2_sdata, key="xenium2.zarr", schema=spatial_schema
).save()
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→ writing the in-memory object into cache
INFO The SpatialData object is not self-contained (i.e. it contains some elements that are Dask-backed from
locations outside /home/runner/.cache/lamindb/AQNNZvAEg7xBnPMe0000.zarr). Please see the documentation of
`is_self_contained()` to understand the implications of working with SpatialData objects that are not
self-contained.
INFO The Zarr backing store has been changed from
/home/runner/.cache/lamindb/lamindata/xenium_aligned_2_guide_min.zarr the new file path:
/home/runner/.cache/lamindb/AQNNZvAEg7xBnPMe0000.zarr
→ loading artifact into memory for validation
INFO The SpatialData object is not self-contained (i.e. it contains some elements that are Dask-backed from
locations outside /home/runner/.cache/lamindb/AQNNZvAEg7xBnPMe0000.zarr). Please see the documentation of
`is_self_contained()` to understand the implications of working with SpatialData objects that are not
self-contained.
! 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': 'cell_id', 'transcript_counts', 'control_probe_counts', 'control_codeword_counts', 'total_counts', 'cell_area', 'nucleus_area', 'region', 'dataset', 'celltype_minor'
→ 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='6cLgKMjC86L4fBl8', name=None, description=None, n=4, is_type=False, itype='Feature', otype=None, dtype=None, hash='L1oI2t9ZRMJIZ-HpbugLJQ', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=1, run_id=1, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-10-30 18:59:35 UTC, is_locked=False)
→ returning schema with same hash: Schema(uid='MIQhXQY4peBBBfj8', name=None, description=None, n=1, is_type=False, itype='Feature', otype=None, dtype=None, hash='n3UpwCoB80EtvbjvdpRAqA', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=1, run_id=1, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-10-30 18:59:35 UTC, is_locked=False)
→ returning schema with same hash: Schema(uid='NW8wNBdsAkkr22X3', 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=1, run_id=1, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-10-30 18:59:35 UTC, is_locked=False)
xenium_2_curated_af.describe()
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Artifact: xenium2.zarr (0000) ├── uid: AQNNZvAEg7xBnPMe0000 run: BQdBaxy (spatial.ipynb) │ kind: dataset otype: SpatialData │ hash: noI1oD6jyNbhK3yysxHjAw size: 38.9 MB │ branch: main space: all │ created_at: 2025-10-30 18:59:39 UTC created_by: testuser1 │ n_files: 177 ├── storage/path: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/AQNNZvAEg7xBnPMe.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 └── .projects Project spatial guide datasets .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 = (
ln.Artifact.using("laminlabs/lamindata")
.get(key="visium_aligned_guide_min.zarr")
.load()
)
visium_aligned_sdata
Show code cell output
→ transferred: Artifact(uid='bjH534dxVi1drmLZ0001')
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
INFO The SpatialData object is not self-contained (i.e. it contains some elements that are Dask-backed from
locations outside /home/runner/.cache/lamindb/OsKRqrNTuvRkHVns0000.zarr). Please see the documentation of
`is_self_contained()` to understand the implications of working with SpatialData objects that are not
self-contained.
INFO The Zarr backing store has been changed from
/home/runner/.cache/lamindb/lamindata/visium_aligned_guide_min.zarr the new file path:
/home/runner/.cache/lamindb/OsKRqrNTuvRkHVns0000.zarr
→ loading artifact into memory for validation
INFO The SpatialData object is not self-contained (i.e. it contains some elements that are Dask-backed from
locations outside /home/runner/.cache/lamindb/OsKRqrNTuvRkHVns0000.zarr). Please see the documentation of
`is_self_contained()` to understand the implications of working with SpatialData objects that are not
self-contained.
! 7 terms not validated in feature 'columns' in slot 'tables:table:obs': 'in_tissue', 'array_row', 'array_col', 'spot_id', 'region', 'dataset', 'clone'
→ 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 organism 'human', 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='6cLgKMjC86L4fBl8', name=None, description=None, n=4, is_type=False, itype='Feature', otype=None, dtype=None, hash='L1oI2t9ZRMJIZ-HpbugLJQ', minimal_set=True, ordered_set=False, maximal_set=False, slot=None, branch_id=1, space_id=1, created_by_id=1, run_id=1, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-10-30 18:59:35 UTC, is_locked=False)
→ returning schema with same hash: Schema(uid='cQs2iMBESo7Pv9z8', 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=1, run_id=1, type_id=None, validated_by_id=None, composite_id=None, created_at=2025-10-30 18:59:17 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: OsKRqrNTuvRkHVns0000 run: BQdBaxy (spatial.ipynb) │ kind: dataset otype: SpatialData │ hash: K5LTYUrbsx9OfVjYLWv1tw size: 5.5 MB │ branch: main space: all │ created_at: 2025-10-30 18:59:52 UTC created_by: testuser1 │ n_files: 136 ├── storage/path: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/OsKRqrNTuvRkHVns.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 └── .projects Project spatial guide datasets .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"])
→ queried for all categorical features with dtype Record and non-categorical features: (0) []
| uid | key | size | hash | |
|---|---|---|---|---|
| id | ||||
| 7 | OsKRqrNTuvRkHVns0000 | visium.zarr | 5810515 | K5LTYUrbsx9OfVjYLWv1tw |
| 6 | bjH534dxVi1drmLZ0001 | visium_aligned_guide_min.zarr | 5809684 | a8rVkf_kjp9To9KI06i03g |
| 5 | AQNNZvAEg7xBnPMe0000 | xenium2.zarr | 40823410 | noI1oD6jyNbhK3yysxHjAw |
| 4 | KFhRNPqcdoxBCNZt0001 | xenium_aligned_2_guide_min.zarr | 40822308 | oH569Lh4koYRB1I6AatnGQ |
| 3 | 3AYeC9diJJliwPdS0000 | xenium1.zarr | 35116259 | 68_dzfaDidoKacs0glIGNg |
| 2 | kVMuYil81BHTwQ9G0001 | xenium_aligned_1_guide_min.zarr | 35115305 | 8f1qC6IkpSvFw2H8TdhplQ |
| 1 | cyXf0Vym1gBwRMIr0000 | example_blobs.zarr | 12122461 | LZ9HLHkw8HhoOzS7Vt_VSg |
ln.finish()
Show code cell output
→ finished Run('BQdBaxymLEK5qCV1') after 40s at 2025-10-30 18:59:54 UTC