10x Xenium
Human pancreas
pancreatic-cancer-with-xenium-human-multi-tissue-and-cancer-panel-1-standard
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'he_image': DataTree[cyx] (3, 71883, 20562), (3, 35941, 10281), (3, 17970, 5140), (3, 8985, 2570), (3, 4492, 1285)
│ ├── 'morphology_focus': DataTree[cyx] (1, 13752, 48274), (1, 6876, 24137), (1, 3438, 12068), (1, 1719, 6034), (1, 859, 3017)
│ └── 'morphology_mip': DataTree[cyx] (1, 13752, 48274), (1, 6876, 24137), (1, 3438, 12068), (1, 1719, 6034), (1, 859, 3017)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (13752, 48274), (6876, 24137), (3438, 12068), (1719, 6034), (859, 3017)
│ └── 'nucleus_labels': DataTree[yx] (13752, 48274), (6876, 24137), (3438, 12068), (1719, 6034), (859, 3017)
├── Points
│ └── 'transcripts': DataFrame with shape: (28455659, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (190965, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (190965, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (190965, 1) (2D shapes)
└── Tables
└── 'table': AnnData (190965, 474)
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("JaMFDc7tfglYPDnl") # 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/JaMFDc7tfglYPDnl
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid JaMFDc7tfglYPDnl Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["JaMFDc7tfglYPDnl0001"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- pancreas
- Modality
- RNA
- Observations
- 190,965
- Transcripts
- 28,455,659
- Tier
- silver
- Chemistry
- Xenium v1
- Magnification
- 40x
- Resolution
- 20,562 × 71,883 px
- mpp
- 0.002 µm/px
- Size
- 6.4 GB
- Sample ID
- pancreatic-cancer-with-xenium-human-multi-tissue-and-cancer-panel-1-standard
- License
- CC BY 4.0
- Published
- Nov 15, 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