Visium
Human breast
GSM6433589
bronze Interactive viewer H&E triple-negative breast carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'GSM6433589_093C_hires_image': DataArray[cyx] (3, 1928, 2000)
├── Shapes
│ └── 'GSM6433589_093C': GeoDataFrame shape: (2117, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2117, 36601)
with coordinate systems:
▸ 'GSM6433589_093C', with elements:
GSM6433589_093C_hires_image (Images), GSM6433589_093C (Shapes)
▸ 'GSM6433589_093C_downscaled_hires', with elements:
GSM6433589_093C_hires_image (Images), GSM6433589_093C (Shapes)
▸ 'GSM6433589_093C_downscaled_lowres', with elements:
GSM6433589_093C (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("fFi1vABnqFAD5SPN") # 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/fFi1vABnqFAD5SPN
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid fFi1vABnqFAD5SPN Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["fFi1vABnqFAD5SPN0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- human
- Tissue
- breast
- Condition
- triple-negative breast carcinoma
- Modality
- RNA
- Observations
- 2,117
- Tier
- bronze
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,928 px
- mpp
- 3.788 µm/px
- Size
- 43 MB
- Sample ID
- GSM6433589
- License
- unknown
- Published
- Nov 2, 2022
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB