10x Xenium
Human skin of body
human-skin-preview-data-xenium-human-skin-gene-expression-panel-1-standard
silver Interactive viewer H&E melanoma No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'he_image': DataTree[cyx] (3, 22673, 20342), (3, 11336, 10171), (3, 5668, 5085), (3, 2834, 2542), (3, 1417, 1271)
│ ├── 'morphology_focus': DataTree[cyx] (1, 27276, 31262), (1, 13638, 15631), (1, 6819, 7815), (1, 3409, 3907), (1, 1704, 1953)
│ └── 'morphology_mip': DataTree[cyx] (1, 27276, 31262), (1, 13638, 15631), (1, 6819, 7815), (1, 3409, 3907), (1, 1704, 1953)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (27276, 31262), (13638, 15631), (6819, 7815), (3409, 3907), (1704, 1953)
│ └── 'nucleus_labels': DataTree[yx] (27276, 31262), (13638, 15631), (6819, 7815), (3409, 3907), (1704, 1953)
├── Points
│ └── 'transcripts': DataFrame with shape: (23651907, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (106980, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (106980, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (106980, 1) (2D shapes)
└── Tables
└── 'table': AnnData (106980, 282)
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("yJkswgwZedAf1Q5E") # 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/yJkswgwZedAf1Q5E
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid yJkswgwZedAf1Q5E Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["yJkswgwZedAf1Q5E0001"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- skin of body
- Condition
- melanoma
- Modality
- RNA
- Observations
- 106,980
- Transcripts
- 23,651,907
- Tier
- silver
- Chemistry
- Xenium v1
- Magnification
- 40x
- Resolution
- 20,342 × 22,673 px
- mpp
- 0.001 µm/px
- Size
- 3.2 GB
- Sample ID
- human-skin-preview-data-xenium-human-skin-gene-expression-panel-1-standard
- License
- CC BY 4.0
- Published
- Sep 18, 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