10x Xenium
Human liver
human-liver-data-xenium-human-multi-tissue-and-cancer-panel-1-standard__hliver-nondiseased-section-ffpe
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'he_image': DataTree[cyx] (3, 42353, 28183), (3, 21176, 14091), (3, 10588, 7045), (3, 5294, 3522), (3, 2647, 1761)
│ ├── 'morphology_focus': DataTree[cyx] (1, 37409, 53949), (1, 18704, 26974), (1, 9352, 13487), (1, 4676, 6743), (1, 2338, 3371)
│ └── 'morphology_mip': DataTree[cyx] (1, 37409, 53949), (1, 18704, 26974), (1, 9352, 13487), (1, 4676, 6743), (1, 2338, 3371)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (37409, 53949), (18704, 26974), (9352, 13487), (4676, 6743), (2338, 3371)
│ └── 'nucleus_labels': DataTree[yx] (37409, 53949), (18704, 26974), (9352, 13487), (4676, 6743), (2338, 3371)
├── Points
│ └── 'transcripts': DataFrame with shape: (25424111, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (239271, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (239271, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (239271, 1) (2D shapes)
└── Tables
└── 'table': AnnData (239271, 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("jr5KKTKJK1kgewOd") # 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/jr5KKTKJK1kgewOd
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid jr5KKTKJK1kgewOd Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["jr5KKTKJK1kgewOd0001"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
10x-human-liver-data-xenium-human-multi-tissue-and-cancer-panel-1-standard→ 2 datasets · other
Metadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- liver
- Modality
- RNA
- Observations
- 239,271
- Transcripts
- 25,424,111
- Tier
- silver
- Chemistry
- Xenium v1
- Resolution
- 28,183 × 42,353 px
- Size
- 8.3 GB
- Sample ID
- human-liver-data-xenium-human-multi-tissue-and-cancer-panel-1-standard__hliver-nondiseased-section-ffpe
- License
- CC BY 4.0
- Published
- Mar 5, 2024
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