MALDI mass spectrometry imaging
Human kidney
2019-09-11_23h46m54s
bronze Interactive viewer H&E No linked publication
print(sdata)
SpatialData object
├── Images
│ └── 'optical_image': DataTree[cyx] (3, 3291, 3278), (3, 1645, 1639), (3, 822, 819), (3, 411, 409)
├── Shapes
│ └── 'maldi_spots': GeoDataFrame shape: (22260, 2) (2D shapes)
└── Tables
└── 'table': AnnData (22260, 89)
with coordinate systems:
▸ 'global', with elements:
optical_image (Images), maldi_spots (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("jrxsnd6dEF8dkzW20000")
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("jrxsnd6dEF8dkzW2") # 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 jrxsnd6dEF8dkzW20000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/jrxsnd6dEF8dkzW2
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid jrxsnd6dEF8dkzW2License: CC BY 4.0
Metadata
- Technology
- MALDI mass spectrometry imaging
- Organism
- human
- Tissue
- kidney
- Modality
- Metabolomics
- Observations
- 22,260
- Tier
- bronze
- Resolution
- 3,278 × 3,291 px
- mpp
- 30.000 µm/px
- Size
- 21.8 MB
- Sample ID
- 2019-09-11_23h46m54s
- License
- CC BY 4.0
- Published
- Sep 11, 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