Visium
Mouse brain
GSM5773453
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1_full_image': DataTree[cyx] (4, 14592, 14848), (4, 7296, 7424), (4, 3648, 3712), (4, 1824, 1856), (4, 912, 928)
│ └── 'GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1_hires_image': DataArray[cyx] (3, 1966, 2000)
├── Shapes
│ └── 'GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1': GeoDataFrame shape: (3259, 2) (2D shapes)
└── Tables
└── 'table': AnnData (3259, 32285)
with coordinate systems:
▸ 'GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1', with elements:
GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1_full_image (Images), GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1_hires_image (Images), GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1 (Shapes)
▸ 'GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1_downscaled_hires', with elements:
GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1_hires_image (Images), GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1 (Shapes)
▸ 'GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1_downscaled_lowres', with elements:
GSM5773453_Young_mouse_brain_A1-1_home_wucheng_Spatial_mouse_data_New_Folder_Young_mouse_brain_A1-1 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("RbAQYWAzTdB0CYm9") # 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/RbAQYWAzTdB0CYm9
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid RbAQYWAzTdB0CYm9 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["RbAQYWAzTdB0CYm90000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart 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
- 3,259
- Tier
- silver
- Chemistry
- Visium v1
- Resolution
- 2,000 × 1,966 px
- mpp
- 3.436 µm/px
- Size
- 409 MB
- Sample ID
- GSM5773453
- License
- unknown
- Published
- Feb 20, 2024
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB