Visium
Mouse skin of body
GSM7845295
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM7845295_WM4237_T4_S1_ST_SC2200329_hires_image': DataArray[cyx] (3, 2000, 1976)
│ └── 'GSM7845295_WM4237_T4_S1_ST_SC2200329_lowres_image': DataArray[cyx] (3, 600, 593)
├── Shapes
│ └── 'GSM7845295_WM4237_T4_S1_ST_SC2200329': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4992, 68886)
with coordinate systems:
▸ 'GSM7845295_WM4237_T4_S1_ST_SC2200329', with elements:
GSM7845295_WM4237_T4_S1_ST_SC2200329_hires_image (Images), GSM7845295_WM4237_T4_S1_ST_SC2200329_lowres_image (Images), GSM7845295_WM4237_T4_S1_ST_SC2200329 (Shapes)
▸ 'GSM7845295_WM4237_T4_S1_ST_SC2200329_downscaled_hires', with elements:
GSM7845295_WM4237_T4_S1_ST_SC2200329_hires_image (Images), GSM7845295_WM4237_T4_S1_ST_SC2200329 (Shapes)
▸ 'GSM7845295_WM4237_T4_S1_ST_SC2200329_downscaled_lowres', with elements:
GSM7845295_WM4237_T4_S1_ST_SC2200329_lowres_image (Images), GSM7845295_WM4237_T4_S1_ST_SC2200329 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("nNioU1zkUkNEZwrG") # 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/nNioU1zkUkNEZwrG
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid nNioU1zkUkNEZwrG Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["nNioU1zkUkNEZwrG0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of BRAF-mutant melanoma→ 23 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- skin of body
- Modality
- RNA
- Observations
- 4,992
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 1,976 × 2,000 px
- mpp
- 3.407 µm/px
- Size
- 52 MB
- Sample ID
- GSM7845295
- License
- unknown
- Published
- Dec 11, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB