PhenoCycler
Human pancreas
S-BIAD859:6371v1:reg001
silver Interactive viewer type 1 diabetes mellitus ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ └── 'reg001_image': DataTree[cyx] (84, 3528, 3360), (84, 1764, 1680), (84, 882, 840), (84, 441, 420), (84, 220, 210)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (50567, 1) (2D shapes)
│ └── 'cell_boundaries_for_vitessce': GeoDataFrame shape: (50567, 2) (2D shapes)
└── Tables
└── 'table': AnnData (50567, 84)
with coordinate systems:
▸ 'global', with elements:
reg001_image (Images), cell_boundaries (Shapes), cell_boundaries_for_vitessce (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("YRIWXhfhZHe30Vf6") # 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/YRIWXhfhZHe30Vf6
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid YRIWXhfhZHe30Vf6 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["YRIWXhfhZHe30Vf60000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- PhenoCycler
- Organism
- human
- Tissue
- pancreas
- Condition
- type 1 diabetes mellitus
- Modality
- protein
- Observations
- 50,567
- Tier
- silver
- Chemistry
- CODEX Processor
- Staining
- IF
- Size
- 1.9 GB
- Sample ID
- S-BIAD859:6371v1:reg001
- License
- CC0 1.0
- Published
- Dec 1, 2023
Cite this dataset
No linked publication found — please cite the data source: ebi.ac.uk ↗.
Source: ebi.ac.uk ↗ · License: CC0 1.0 ↗ · Reprocessed to SpatialData by SpatialData-DB