Visium
Mouse colon
GSM6505142
silver Interactive viewer H&E adenocarcinoma ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM6505142_MC38_d10_m5_C1-C2_e1_full_image': DataTree[cyx] (3, 41279, 39334), (3, 20639, 19667), (3, 10319, 9833), (3, 5159, 4916), (3, 2579, 2458)
│ └── 'GSM6505142_MC38_d10_m5_C1-C2_e1_hires_image': DataArray[cyx] (3, 2000, 1906)
├── Shapes
│ └── 'GSM6505142_MC38_d10_m5_C1-C2_e1': GeoDataFrame shape: (1378, 2) (2D shapes)
└── Tables
└── 'table': AnnData (1378, 32285)
with coordinate systems:
▸ 'GSM6505142_MC38_d10_m5_C1-C2_e1', with elements:
GSM6505142_MC38_d10_m5_C1-C2_e1_full_image (Images), GSM6505142_MC38_d10_m5_C1-C2_e1_hires_image (Images), GSM6505142_MC38_d10_m5_C1-C2_e1 (Shapes)
▸ 'GSM6505142_MC38_d10_m5_C1-C2_e1_downscaled_hires', with elements:
GSM6505142_MC38_d10_m5_C1-C2_e1_hires_image (Images), GSM6505142_MC38_d10_m5_C1-C2_e1 (Shapes)
▸ 'GSM6505142_MC38_d10_m5_C1-C2_e1_downscaled_lowres', with elements:
GSM6505142_MC38_d10_m5_C1-C2_e1 (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("rlZPiiAfJ7UOZFQP0000")
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("rlZPiiAfJ7UOZFQP") # 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 rlZPiiAfJ7UOZFQP0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/rlZPiiAfJ7UOZFQP
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid rlZPiiAfJ7UOZFQPLicense: NCBI GEO policy
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- colon
- Condition
- adenocarcinoma
- Modality
- RNA
- Observations (spots)
- 1,378
- Tier
- silver
- Chemistry
- Visium v1
- Magnification
- 40x
- Resolution
- 39,334 × 41,279 px
- mpp
- 0.186 µm/px
- Size
- 2.9 GB
- Sample ID
- GSM6505142
- License
- NCBI GEO policy
- Published
- Nov 1, 2022
Cite this dataset
doi.org/10.1016/j.crmeth.2022.100340 ↗ BibTeX
@article{Lyubetskaya_2022,
title = {Assessment of spatial transcriptomics for oncology discovery},
volume = {2},
ISSN = {2667-2375},
url = {http://dx.doi.org/10.1016/j.crmeth.2022.100340},
DOI = {10.1016/j.crmeth.2022.100340},
number = {11},
journal = {Cell Reports Methods},
publisher = {Elsevier BV},
author = {Lyubetskaya, Anna and Rabe, Brian and Fisher, Andrew and Lewin, Anne and Neuhaus, Isaac and Brett, Constance and Brett, Todd and Pereira, Ethel and Golhar, Ryan and Kebede, Sami and Font-Tello, Alba and Mosure, Kathy and Van Wittenberghe, Nicholas and Mavrakis, Konstantinos J. and MacIsaac, Kenzie and Chen, Benjamin J. and Drokhlyansky, Eugene},
year = {2022},
month = Nov,
pages = {100340}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB