CosMx
Human breast
multiomic_breast
silver Interactive viewer breast cancer No linked publication
print(sdata)
SpatialData object
├── Points
│ └── 'transcripts': DataFrame with shape: (1073170806, 12) (2D points)
├── Shapes
│ ├── 'cells_polygons': GeoDataFrame shape: (153832, 9) (2D shapes)
│ ├── 'cells_polygons_for_vitessce': GeoDataFrame shape: (152444, 2) (2D shapes)
│ └── 'fov_boxes': GeoDataFrame shape: (383, 2) (2D shapes)
└── Tables
├── 'protein_table': AnnData (153832, 69)
└── 'table': AnnData (152444, 20385)
with coordinate systems:
▸ 'global', with elements:
transcripts (Points), cells_polygons (Shapes), cells_polygons_for_vitessce (Shapes), fov_boxes (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("K1v57r8i8fQeI0Ul") # 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/K1v57r8i8fQeI0Ul
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid K1v57r8i8fQeI0Ul Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["K1v57r8i8fQeI0Ul0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- CosMx
- Organism
- human
- Tissue
- breast
- Condition
- breast cancer
- Modality
- RNA · protein
- Observations
- 152,444
- Transcripts
- 358,103,480
- Tier
- silver
- Chemistry
- CosMx Multiomics
- Size
- 4.8 GB
- Sample ID
- multiomic_breast
- License
- unknown
- Published
- Dec 18, 2025
Cite this dataset
No linked publication found — please cite the data source: brukerspatialbiology.com ↗.
Source: brukerspatialbiology.com ↗ · Reprocessed to SpatialData by SpatialData-DB