Visium
Human ovary
GSM8207506
silver Interactive viewer H&E ovarian serous adenocarcinoma No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'GSM8207506_CPS_OV24RTOV4_full_image': DataTree[cyx] (3, 3085, 2953), (3, 1542, 1476), (3, 771, 738), (3, 385, 369), (3, 192, 184)
├── Shapes
│ └── 'GSM8207506_CPS_OV24RTOV4': GeoDataFrame shape: (4523, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4523, 36601)
with coordinate systems:
▸ 'GSM8207506_CPS_OV24RTOV4', with elements:
GSM8207506_CPS_OV24RTOV4_full_image (Images), GSM8207506_CPS_OV24RTOV4 (Shapes)
▸ 'GSM8207506_CPS_OV24RTOV4_downscaled_hires', with elements:
GSM8207506_CPS_OV24RTOV4 (Shapes)
▸ 'GSM8207506_CPS_OV24RTOV4_downscaled_lowres', with elements:
GSM8207506_CPS_OV24RTOV4 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("lQo4xi4sz7cfHnHa") # 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/lQo4xi4sz7cfHnHa
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid lQo4xi4sz7cfHnHa Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["lQo4xi4sz7cfHnHa0000"])[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
- 4,523
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,953 × 3,085 px
- mpp
- 2.309 µm/px
- Size
- 70 MB
- Sample ID
- GSM8207506
- 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