10x Xenium
Human kidney
human-kidney-preview-data-xenium-human-multi-tissue-and-cancer-panel-1-standard__hkidney-nondiseased-section
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'he_image': DataTree[cyx] (3, 33427, 11949), (3, 16713, 5974), (3, 8356, 2987), (3, 4178, 1493), (3, 2089, 746)
│ ├── 'morphology_focus': DataTree[cyx] (1, 13921, 42636), (1, 6960, 21318), (1, 3480, 10659), (1, 1740, 5329), (1, 870, 2664)
│ └── 'morphology_mip': DataTree[cyx] (1, 13921, 42636), (1, 6960, 21318), (1, 3480, 10659), (1, 1740, 5329), (1, 870, 2664)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (13921, 42636), (6960, 21318), (3480, 10659), (1740, 5329), (870, 2664)
│ └── 'nucleus_labels': DataTree[yx] (13921, 42636), (6960, 21318), (3480, 10659), (1740, 5329), (870, 2664)
├── Points
│ └── 'transcripts': DataFrame with shape: (8090108, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (97560, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (97560, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (97560, 1) (2D shapes)
└── Tables
└── 'table': AnnData (97560, 377)
with coordinate systems:
▸ 'global', with elements:
he_image (Images), morphology_focus (Images), morphology_mip (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("RwEKt4wbzJsqG4yK") # latest version
sdata = artifact.load() # cache, then load into memory
group = artifact.open() # stream without downloading
path = artifact.cache() # sync into the local cache, returns the path Load from the CLI
# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/RwEKt4wbzJsqG4yK
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid RwEKt4wbzJsqG4yK Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["RwEKt4wbzJsqG4yK0001"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
10x-human-kidney-preview-data-xenium-human-multi-tissue-and-cancer-panel-1-standard→ 2 datasets · other
Metadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- kidney
- Modality
- RNA
- Observations
- 97,560
- Transcripts
- 8,090,108
- Tier
- silver
- Chemistry
- Xenium v1
- Magnification
- 40x
- Resolution
- 11,949 × 33,427 px
- mpp
- 0.012 µm/px
- Size
- 2.7 GB
- Sample ID
- human-kidney-preview-data-xenium-human-multi-tissue-and-cancer-panel-1-standard__hkidney-nondiseased-section
- License
- CC BY 4.0
- Published
- Jul 17, 2023
Cite this dataset
No linked publication found — please cite the data source: 10x Genomics ↗.
Source: 10x Genomics ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB