MALDI mass spectrometry imaging
Human kidney
2020-01-06_22h02m51s
bronze Interactive viewer H&E ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ └── 'optical_image': DataTree[cyx] (3, 763, 900), (3, 381, 450), (3, 190, 225), (3, 95, 112)
├── Shapes
│ └── 'maldi_spots': GeoDataFrame shape: (27313, 2) (2D shapes)
└── Tables
└── 'table': AnnData (27313, 98)
with coordinate systems:
▸ 'global', with elements:
optical_image (Images), maldi_spots (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("fF2M7WUbkFnMMhpC") # 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/fF2M7WUbkFnMMhpC
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid fF2M7WUbkFnMMhpC Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["fF2M7WUbkFnMMhpC0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openMetadata
- Technology
- MALDI mass spectrometry imaging
- Organism
- human
- Tissue
- kidney
- Modality
- Metabolomics
- Tier
- bronze
- Resolution
- 900 × 763 px
- mpp
- 50.000 µm/px
- Size
- 10 MB
- Sample ID
- 2020-01-06_22h02m51s
- License
- CC BY 4.0
- Published
- Sep 28, 2020
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