Visium
Mouse kidney
mouse-tissue-microarray-in-5x5-layout-with-1-mm-edge-to-edge-spacing-ffpe-2-standard
silver Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ ├── 'CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_full_image': DataTree[cyx] (3, 33196, 34284), (3, 16598, 17142), (3, 8299, 8571), (3, 4149, 4285), (3, 2074, 2142)
│ ├── 'CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_hires_image': DataArray[cyx] (3, 1937, 2000)
│ └── 'CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_lowres_image': DataArray[cyx] (3, 581, 600)
├── Shapes
│ └── 'CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm': GeoDataFrame shape: (2805, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2805, 19465)
with coordinate systems:
▸ 'CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm', with elements:
CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_full_image (Images), CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_hires_image (Images), CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_lowres_image (Images), CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm (Shapes)
▸ 'CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_downscaled_hires', with elements:
CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_hires_image (Images), CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm (Shapes)
▸ 'CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_downscaled_lowres', with elements:
CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm_lowres_image (Images), CytAssist_11mm_FFPE_Mouse_TMA_5x5_1mm (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("1uZYdlOkO6lJ01us") # 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/1uZYdlOkO6lJ01us
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 1uZYdlOkO6lJ01us Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["1uZYdlOkO6lJ01us0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- kidney
- Modality
- RNA
- Observations
- 2,805
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Magnification
- 40x
- Resolution
- 34,284 × 33,196 px
- mpp
- 0.251 µm/px
- Size
- 2.2 GB
- Sample ID
- mouse-tissue-microarray-in-5x5-layout-with-1-mm-edge-to-edge-spacing-ffpe-2-standard
- License
- CC BY 4.0
- Published
- Feb 21, 2023
Cite this dataset
No linked publication found — please cite the data source: 10x Genomics ↗.
Source: 10x Genomics ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB