Visium
Mouse brain
GSM5773456
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1_full_image': DataTree[cyx] (4, 14592, 14592), (4, 7296, 7296), (4, 3648, 3648), (4, 1824, 1824), (4, 912, 912)
│ └── 'GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1_hires_image': DataArray[cyx] (3, 2000, 2000)
├── Shapes
│ └── 'GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1': GeoDataFrame shape: (3294, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3294, 32285)
with coordinate systems:
▸ 'GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1', with elements:
GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1_full_image (Images), GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1_hires_image (Images), GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1 (Shapes)
▸ 'GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1_downscaled_hires', with elements:
GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1_hires_image (Images), GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1 (Shapes)
▸ 'GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1_downscaled_lowres', with elements:
GSM5773456_Young_mouse_brain_D1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_D1-1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("GYk7QdaBLKfQz8HN0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) Python · lamindb
Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("GYk7QdaBLKfQz8HN") # stem uid: Lamin resolves it to the 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download GYk7QdaBLKfQz8HN0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/GYk7QdaBLKfQz8HN
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid GYk7QdaBLKfQz8HNLicense: NCBI GEO policy
Part of collection
Spatial transcriptomic profiling of the aging mouse brain [spatial scRNA-seq]→ 8 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- brain
- Modality
- RNA
- Observations (spots)
- 3,294
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 2,000 px
- mpp
- 3.375 µm/px
- Size
- 402 MB
- Sample ID
- GSM5773456
- License
- NCBI GEO policy
- Published
- Feb 20, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB