Visium
Human ovary
GSM8207500
silver Interactive viewer H&E ovarian serous adenocarcinoma No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'GSM8207500_CPS_OV34RtOV1_full_image': DataTree[cyx] (3, 3003, 2944), (3, 1501, 1472), (3, 750, 736), (3, 375, 368), (3, 187, 184)
├── Shapes
│ └── 'GSM8207500_CPS_OV34RtOV1': GeoDataFrame shape: (4274, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4274, 36601)
with coordinate systems:
▸ 'GSM8207500_CPS_OV34RtOV1', with elements:
GSM8207500_CPS_OV34RtOV1_full_image (Images), GSM8207500_CPS_OV34RtOV1 (Shapes)
▸ 'GSM8207500_CPS_OV34RtOV1_downscaled_hires', with elements:
GSM8207500_CPS_OV34RtOV1 (Shapes)
▸ 'GSM8207500_CPS_OV34RtOV1_downscaled_lowres', with elements:
GSM8207500_CPS_OV34RtOV1 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("IRGrxigbE1epIXj5") # 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/IRGrxigbE1epIXj5
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid IRGrxigbE1epIXj5 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["IRGrxigbE1epIXj50000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Analysis of Spatial Transcriptomics for Unveiling the novel molecular characteristics of Recurrent and Non-Recurrent High-Grade Serous Ovarian Cancer→ 8 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- ovary
- Condition
- ovarian serous adenocarcinoma
- Modality
- RNA
- Observations
- 4,274
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,944 × 3,003 px
- mpp
- 2.230 µm/px
- Size
- 61 MB
- Sample ID
- GSM8207500
- License
- unknown
- Published
- Apr 18, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB