Visium
Human liver
GSM8291069
silver Interactive viewer H&E breast carcinoma ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8291069_V42Y04-372_A1_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM8291069_V42Y04-372_A1_hires_image': DataArray[cyx] (3, 1634, 2000)
│ └── 'GSM8291069_V42Y04-372_A1_lowres_image': DataArray[cyx] (3, 490, 600)
├── Shapes
│ └── 'GSM8291069_V42Y04-372_A1': GeoDataFrame shape: (2848, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2848, 18085)
with coordinate systems:
▸ 'GSM8291069_V42Y04-372_A1', with elements:
GSM8291069_V42Y04-372_A1_full_image (Images), GSM8291069_V42Y04-372_A1_hires_image (Images), GSM8291069_V42Y04-372_A1_lowres_image (Images), GSM8291069_V42Y04-372_A1 (Shapes)
▸ 'GSM8291069_V42Y04-372_A1_downscaled_hires', with elements:
GSM8291069_V42Y04-372_A1_hires_image (Images), GSM8291069_V42Y04-372_A1 (Shapes)
▸ 'GSM8291069_V42Y04-372_A1_downscaled_lowres', with elements:
GSM8291069_V42Y04-372_A1_lowres_image (Images), GSM8291069_V42Y04-372_A1 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("VZl6d5uoogwaDAFz") # 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/VZl6d5uoogwaDAFz
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid VZl6d5uoogwaDAFz Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["VZl6d5uoogwaDAFz0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
snPATHO-seq: unlocking the FFPE archives for single nucleus RNA profiling [Visium]→ 3 datasets · comparison
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- liver
- Condition
- breast carcinoma
- Modality
- RNA
- Observations
- 2,848
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 2,000 × 1,634 px
- mpp
- 5.147 µm/px
- Size
- 86 MB
- Sample ID
- GSM8291069
- License
- NCBI GEO policy
- Published
- Oct 1, 2024
Cite this dataset
doi.org/10.1038/s42003-024-07043-2 ↗ BibTeX
@article{Wang_2024,
title = {snPATHO-seq, a versatile FFPE single-nucleus RNA sequencing method to unlock pathology archives},
volume = {7},
ISSN = {2399-3642},
url = {http://dx.doi.org/10.1038/s42003-024-07043-2},
DOI = {10.1038/s42003-024-07043-2},
number = {1},
journal = {Communications Biology},
publisher = {Springer Science and Business Media LLC},
author = {Wang, Taopeng and Roach, Michael J. and Harvey, Kate and Morlanes, Javier Escudero and Kiedik, Beata and Al-Eryani, Ghamdan and Greenwald, Alissa and Kalavros, Nikolaos and Dezem, Felipe Segato and Ma, Yuling and Pita-Juarez, Yered H. and Wise, Kellie and Degletagne, Cyril and Elz, Anna and Hadadianpour, Azi and Johanneson, Jack and Pakiam, Fiona and Ryu, Heeju and Newell, Evan W. and Tonon, Laurie and Kohlway, Andrew and Drennon, Tingsheng and Abousoud, Jawad and Stott, Ryan and Lund, Paul and Durruthy, Jens and Vallejo, Andres F. and Li, Wenyan and Salomon, Robert and Kaczorowski, Dominik and Warren, Joanna and Butler, Lisa M. and O’Toole, Sandra and Plummer, Jasmine and Vlachos, Ioannis S. and Lundeberg, Joakim and Swarbrick, Alexander and Martelotto, Luciano G.},
year = {2024},
month = Oct
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB