PhenoCycler-Fusion
Human bone marrow
25123538:AML2_183_Scan1
silver Interactive viewer acute myeloid leukemia ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ └── 'AML2_183_image': DataTree[cyx] (54, 10800, 7680), (54, 5400, 3840), (54, 2700, 1920), (54, 1350, 960), (54, 675, 480)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (95273, 1) (2D shapes)
│ └── 'cell_boundaries_for_vitessce': GeoDataFrame shape: (95273, 2) (2D shapes)
└── Tables
└── 'table': AnnData (95273, 54)
with coordinate systems:
▸ 'global', with elements:
AML2_183_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("qiNYqWzFkvOOrbYT") # 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/qiNYqWzFkvOOrbYT
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid qiNYqWzFkvOOrbYT Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["qiNYqWzFkvOOrbYT0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- PhenoCycler-Fusion
- Organism
- human
- Tissue
- bone marrow
- Condition
- acute myeloid leukemia
- Modality
- protein
- Observations
- 95,273
- Tier
- silver
- Chemistry
- PhenoCycler-Fusion 1
- Staining
- IF
- Size
- 996 MB
- Sample ID
- 25123538:AML2_183_Scan1
- License
- CC0 1.0
- Published
- Apr 12, 2024
Cite this dataset
No linked publication found — please cite the data source: doi.org ↗.
Source: doi.org ↗ · License: CC0 1.0 ↗ · Reprocessed to SpatialData by SpatialData-DB