MALDI mass spectrometry imaging
Human kidney
2019-12-27_20h42m26s
bronze Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'optical_image': DataTree[cyx] (3, 667, 900), (3, 333, 450), (3, 166, 225), (3, 83, 112)
├── Shapes
│ └── 'maldi_spots': GeoDataFrame shape: (15876, 2) (2D shapes)
└── Tables
└── 'table': AnnData (15876, 117)
with coordinate systems:
▸ 'global', with elements:
optical_image (Images), maldi_spots (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("BnVxvCfDXtl96FK10000")
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("BnVxvCfDXtl96FK1") # 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 BnVxvCfDXtl96FK10000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/BnVxvCfDXtl96FK1
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid BnVxvCfDXtl96FK1License: CC BY 4.0
Metadata
- Technology
- MALDI mass spectrometry imaging
- Organism
- human
- Tissue
- kidney
- Modality
- Metabolomics
- Observations
- 15,876
- Tier
- bronze
- Resolution
- 900 × 667 px
- mpp
- 50.000 µm/px
- Size
- 7.4 MB
- Sample ID
- 2019-12-27_20h42m26s
- License
- CC BY 4.0
- Published
- Dec 27, 2019
Cite this dataset
No linked publication found — please cite the data source: metaspace2020.org ↗.
Source: metaspace2020.org ↗ · License: CC BY 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB