Visium
Mouse kidney
GSM5543483
silver Interactive viewer H&E acute kidney failure No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_hires_image': DataArray[cyx] (3, 1930, 2000)
│ └── 'GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_lowres_image': DataArray[cyx] (3, 579, 600)
├── Shapes
│ └── 'GSM5543483_f6wks_110_processed_f6wks_110_processed_outs': GeoDataFrame shape: (1598, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1598, 32285)
with coordinate systems:
▸ 'GSM5543483_f6wks_110_processed_f6wks_110_processed_outs', with elements:
GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_hires_image (Images), GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_lowres_image (Images), GSM5543483_f6wks_110_processed_f6wks_110_processed_outs (Shapes)
▸ 'GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_downscaled_hires', with elements:
GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_hires_image (Images), GSM5543483_f6wks_110_processed_f6wks_110_processed_outs (Shapes)
▸ 'GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_downscaled_lowres', with elements:
GSM5543483_f6wks_110_processed_f6wks_110_processed_outs_lowres_image (Images), GSM5543483_f6wks_110_processed_f6wks_110_processed_outs (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("2BjfNGIrAIYZkuqa") # 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/2BjfNGIrAIYZkuqa
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 2BjfNGIrAIYZkuqa Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["2BjfNGIrAIYZkuqa0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Spatially resolved transcriptomic analysis of acute kidney injury in a female murine model→ 5 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- kidney
- Condition
- acute kidney failure
- Modality
- RNA
- Observations
- 1,598
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,930 px
- mpp
- 3.310 µm/px
- Size
- 23 MB
- Sample ID
- GSM5543483
- License
- unknown
- Published
- Nov 22, 2021
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB