Visium
Human pancreas
GSM8973752
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8973752_HPAP043_Pancreas_hires_image': DataArray[cyx] (3, 2000, 1222)
│ └── 'GSM8973752_HPAP043_Pancreas_lowres_image': DataArray[cyx] (3, 600, 367)
├── Shapes
│ └── 'GSM8973752_HPAP043_Pancreas': GeoDataFrame shape: (6641, 2) (2D shapes)
└── Tables
└── 'table': AnnData (6641, 18085)
with coordinate systems:
▸ 'GSM8973752_HPAP043_Pancreas', with elements:
GSM8973752_HPAP043_Pancreas_hires_image (Images), GSM8973752_HPAP043_Pancreas_lowres_image (Images), GSM8973752_HPAP043_Pancreas (Shapes)
▸ 'GSM8973752_HPAP043_Pancreas_downscaled_hires', with elements:
GSM8973752_HPAP043_Pancreas_hires_image (Images), GSM8973752_HPAP043_Pancreas (Shapes)
▸ 'GSM8973752_HPAP043_Pancreas_downscaled_lowres', with elements:
GSM8973752_HPAP043_Pancreas_lowres_image (Images), GSM8973752_HPAP043_Pancreas (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("knQwDtvDOHDyad0R0000")
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("knQwDtvDOHDyad0R") # 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 knQwDtvDOHDyad0R0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/knQwDtvDOHDyad0R
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid knQwDtvDOHDyad0RLicense: NCBI GEO policy
Part of collection
Spatial transcriptomics from paired pancreas and associated draining lymph nodes reveals a lymphotoxin-beta signature in human type 1 diabetes→ 35 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- pancreas
- Modality
- RNA
- Observations (spots)
- 6,641
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 1,222 × 2,000 px
- mpp
- 7.909 µm/px
- Size
- 79.7 MB
- Sample ID
- GSM8973752
- License
- NCBI GEO policy
- Published
- Jun 23, 2025
Cite this dataset
doi.org/10.1101/2025.05.19.654940 ↗ BibTeX
@article{Medina_Serpas_2025,
title = {Spatial transcriptomics from pancreas and local draining lymph node tissue reveals a lymphotoxin-β signature in human type 1 diabetes},
url = {http://dx.doi.org/10.1101/2025.05.19.654940},
DOI = {10.1101/2025.05.19.654940},
publisher = {openRxiv},
author = {Medina-Serpas, Miguel A. and Brusko, Maigan and Golden, Gregory J. and Campbell-Thompson, Martha and Rogers, Trevor and Posgai, Amanda L. and Luning Prak, Eline T. and Liu, Chengyang and Kaestner, Klaus H. and Naji, Ali and Betts, Michael R. and McIntyre, Lauren M. and Atkinson, Mark A. and Brusko, Todd M.},
year = {2025},
month = May
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB