Jupyter Notebook

Spatial

Here, you’ll learn how to manage spatial datasets:

  1. curate and ingest spatial data (spatial1/4)

  2. query & analyze spatial datasets (spatial2/4)

  3. load the collection into memory & train a ML model (spatial3/4)

  4. create and share interactive visualizations with vitessce (spatial4/4)

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
 created Transform('Wlnlw4uGNtsd0000'), started new Run('RSjslHXL...') at 2025-05-08 07:31:45 UTC
 notebook imports: bionty==1.3.2 lamindb==1.5.0 spatialdata==0.4.0
 recommendation: to identify the notebook across renames, pass the uid: ln.track("Wlnlw4uGNtsd", project="Project(uid='8QiGDbROAd5Z', name='spatial guide datasets', is_type=False, space_id=1, created_by_id=1, created_at=2025-05-08 07:31:44 UTC)")

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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INFO     The Zarr backing store has been changed from None the new file path:                                      
         /home/runner/.cache/lamindb/HRgs5H2KDnbPd7O70000.zarr                                                     
Artifact(uid='HRgs5H2KDnbPd7O70000', is_latest=True, key='example_blobs.zarr', suffix='.zarr', kind='dataset', otype='SpatialData', size=12121751, hash='Z5I8uWNwd6aRIFCP8nhpRg', n_files=113, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-05-08 07:31:47 UTC)

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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_images/67530c1ddb699da622760bb7f149da1f1474d84567c79ab3e14e2a3b419bf37b.svg

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:

  1. Dataset level metadata is stored in sdata.attrs["sample"].

  2. 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 records: 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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! 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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! 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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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/j8vREOoQ8dhB76mD0000.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/j8vREOoQ8dhB76mD0000.zarr                                                     
xenium_1_curated_af.describe()
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Artifact .zarr/SpatialData
├── General
│   ├── .uid = 'j8vREOoQ8dhB76mD0000'
│   ├── .key = 'xenium1.zarr'
│   ├── .size = 35115549
│   ├── .hash = 'LijLSjFPrD3ouImnR8PXCQ'
│   ├── .n_files = 145
│   ├── .path = /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/j8vREOoQ8dhB76mD.zarr
│   ├── .created_by = testuser1 (Test User1)
│   ├── .created_at = 2025-05-08 07:32:11
│   └── .transform = 'Spatial'
├── Dataset features
│   ├── attrs:sample4            [Feature]                                                           
│   │   assay                       cat[bionty.ExperimentalF…  10x Xenium                               
│   │   disease                     cat[bionty.Disease]        ductal breast carcinoma in situ          
│   │   organism                    cat[bionty.Organism]       human                                    
│   │   tissue                      cat[bionty.Tissue]         breast                                   
│   ├── tables:table:obs1        [Feature]                                                           
│   │   celltype_major              cat[bionty.CellType]       B cell, T cell, cancer associated fibrob…
│   └── tables:table:var.T313    [bionty.Gene.ensembl_gen…                                           
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, perivasc…
        .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 records: 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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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/pl3LHQIMWwwO0Sol0000.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/pl3LHQIMWwwO0Sol0000.zarr                                                     
 returning existing schema with same hash: Schema(uid='GZB9vaBdLtL7FKzi', n=4, is_type=False, itype='Feature', hash='2BbHj0GUD22iZXjg-sNMwA', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-08 07:32:11 UTC)
 returning existing schema with same hash: Schema(uid='kiehSLtXSLqYLEIL', n=1, is_type=False, itype='Feature', hash='Q1B8NMuyoZl4rQVlo3OFxA', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-08 07:32:11 UTC)
 returning existing schema with same hash: Schema(uid='sbJEbnLLhy5QejQK', n=313, is_type=False, itype='bionty.Gene.ensembl_gene_id', dtype='num', hash='FFFt-2qmlVALrsMUPNoH0g', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-08 07:32:11 UTC)
xenium_2_curated_af.describe()
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Artifact .zarr/SpatialData
├── General
│   ├── .uid = 'pl3LHQIMWwwO0Sol0000'
│   ├── .key = 'xenium2.zarr'
│   ├── .size = 40822700
│   ├── .hash = 'VUHssxxZwNA_yRUJhI0VLA'
│   ├── .n_files = 174
│   ├── .path = /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/pl3LHQIMWwwO0Sol.zarr
│   ├── .created_by = testuser1 (Test User1)
│   ├── .created_at = 2025-05-08 07:32:28
│   └── .transform = 'Spatial'
├── Dataset features
│   ├── attrs:sample4            [Feature]                                                           
│   │   assay                       cat[bionty.ExperimentalF…  10x Xenium                               
│   │   disease                     cat[bionty.Disease]        ductal breast carcinoma in situ          
│   │   organism                    cat[bionty.Organism]       human                                    
│   │   tissue                      cat[bionty.Tissue]         breast                                   
│   ├── tables:table:obs1        [Feature]                                                           
│   │   celltype_major              cat[bionty.CellType]       B cell, T cell, cancer associated fibrob…
│   └── tables:table:var.T313    [bionty.Gene.ensembl_gen…                                           
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, perivasc…
        .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
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 transferred records: 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()
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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/UvrBBo0dNITsfnAs0000.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/UvrBBo0dNITsfnAs0000.zarr                                                     
! 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')
! 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 existing schema with same hash: Schema(uid='GZB9vaBdLtL7FKzi', n=4, is_type=False, itype='Feature', hash='2BbHj0GUD22iZXjg-sNMwA', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-08 07:32:11 UTC)
! no features found for slot tables:table:obs
 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()
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Artifact .zarr/SpatialData
├── General
│   ├── .uid = 'UvrBBo0dNITsfnAs0000'
│   ├── .key = 'visium.zarr'
│   ├── .size = 5809805
│   ├── .hash = 'Fy1B1_QWlmie4PEr5KQziA'
│   ├── .n_files = 133
│   ├── .path = /home/runner/work/lamin-usecases/lamin-usecases/docs/test-spatial/.lamindb/UvrBBo0dNITsfnAs.zarr
│   ├── .created_by = testuser1 (Test User1)
│   ├── .created_at = 2025-05-08 07:32:50
│   └── .transform = 'Spatial'
├── Dataset features
│   ├── attrs:sample4            [Feature]                                                           
│   │   assay                       cat[bionty.ExperimentalF…  Visium Spatial Gene Expression           
│   │   disease                     cat[bionty.Disease]        ductal breast carcinoma in situ          
│   │   organism                    cat[bionty.Organism]       human                                    
│   │   tissue                      cat[bionty.Tissue]         breast                                   
│   └── tables:table:var.T18068  [bionty.Gene.ensembl_gen…                                           
└── 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()
_images/1b749acf68d76037219d2f306575f4d307291b2bb4a08e10e49b6a968bc41dbd.svg
ln.Artifact.df(features=True, include=["hash", "size"])
 queried for all categorical features with dtype 'cat[ULabel...'] and non-categorical features: (0) []
uid key size hash
id
7 UvrBBo0dNITsfnAs0000 visium.zarr 5809805 Fy1B1_QWlmie4PEr5KQziA
5 pl3LHQIMWwwO0Sol0000 xenium2.zarr 40822700 VUHssxxZwNA_yRUJhI0VLA
3 j8vREOoQ8dhB76mD0000 xenium1.zarr 35115549 LijLSjFPrD3ouImnR8PXCQ
1 HRgs5H2KDnbPd7O70000 example_blobs.zarr 12121751 Z5I8uWNwd6aRIFCP8nhpRg
4 KFhRNPqcdoxBCNZt0001 xenium_aligned_2_guide_min.zarr 40822308 oH569Lh4koYRB1I6AatnGQ
2 kVMuYil81BHTwQ9G0001 xenium_aligned_1_guide_min.zarr 35115305 8f1qC6IkpSvFw2H8TdhplQ
6 bjH534dxVi1drmLZ0001 visium_aligned_guide_min.zarr 5809684 a8rVkf_kjp9To9KI06i03g
ln.finish()
Hide code cell output
 finished Run('RSjslHXL') after 1m at 2025-05-08 07:32:58 UTC