10x Xenium
Human skin of body
human-skin-data-xenium-human-multi-tissue-and-cancer-panel-1-standard__hskin-nondiseased-section-2-ffpe
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'he_image': DataTree[cyx] (3, 33606, 19058), (3, 16803, 9529), (3, 8401, 4764), (3, 4200, 2382), (3, 2100, 1191)
│ ├── 'morphology_focus': DataTree[cyx] (1, 27278, 45444), (1, 13639, 22722), (1, 6819, 11361), (1, 3409, 5680), (1, 1704, 2840)
│ └── 'morphology_mip': DataTree[cyx] (1, 27278, 45444), (1, 13639, 22722), (1, 6819, 11361), (1, 3409, 5680), (1, 1704, 2840)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (27278, 45444), (13639, 22722), (6819, 11361), (3409, 5680), (1704, 2840)
│ └── 'nucleus_labels': DataTree[yx] (27278, 45444), (13639, 22722), (6819, 11361), (3409, 5680), (1704, 2840)
├── Points
│ └── 'transcripts': DataFrame with shape: (1982951, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (90106, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (90106, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (90106, 1) (2D shapes)
└── Tables
└── 'table': AnnData (90106, 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("LCAXoD0ctWEYb92Y") # 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/LCAXoD0ctWEYb92Y
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid LCAXoD0ctWEYb92Y Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["LCAXoD0ctWEYb92Y0001"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
10x-human-skin-data-xenium-human-multi-tissue-and-cancer-panel-1-standard→ 2 datasets · other
Metadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- skin of body
- Modality
- RNA
- Observations
- 90,106
- Transcripts
- 1,982,951
- Tier
- silver
- Chemistry
- Xenium v1
- Magnification
- 40x
- Resolution
- 19,058 × 33,606 px
- mpp
- 0.001 µm/px
- Size
- 4.2 GB
- Sample ID
- human-skin-data-xenium-human-multi-tissue-and-cancer-panel-1-standard__hskin-nondiseased-section-2-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