Visium

Human uterus

NCBI691

silver Interactive viewer H&E endometriosis ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     └── 'NCBI691_full_image': DataTree[cyx] (3, 15950, 15740), (3, 7975, 7870), (3, 3987, 3935), (3, 1993, 1967)
├── Shapes
│     └── 'NCBI691_spots': GeoDataFrame shape: (1388, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1388, 36601)
with coordinate systems:
    ▸ 'global', with elements:
        NCBI691_full_image (Images), NCBI691_spots (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("vJOkFGOWb7FOEdU20000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)

Install (Python 3.12+) Docs →

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("vJOkFGOWb7FOEdU2")   # 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

Install (Python 3.12+) Docs →

Shell

# sddb client: download the .zarr into data/
sddb-cli download vJOkFGOWb7FOEdU20000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/vJOkFGOWb7FOEdU2

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid vJOkFGOWb7FOEdU2

Install (Python 3.12+) Docs →

License: CC BY-NC-SA 4.0 (noncommercial)

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Part of collection

A single-cell transcriptomic analysis of endometriosis→

2 datasets · publication

Metadata

Technology: Visium
Organism: human
Tissue: uterus
Condition: endometriosis
Modality: RNA
Observations (spots): 1,388
Tier: silver
Chemistry: Visium v1
Magnification: 20x
Resolution: 15,740 × 15,950 px
mpp: 0.504 µm/px
Size: 537 MB
Sample ID: NCBI691
License: CC BY-NC-SA 4.0 NonCommercial
Published: Oct 30, 2022

Cite this dataset

doi.org/10.1038/s41588-022-01254-1 ↗

BibTeX

@article{Fonseca_2023,
  title = {Single-cell transcriptomic analysis of endometriosis},
  volume = {55},
  ISSN = {1546-1718},
  url = {http://dx.doi.org/10.1038/s41588-022-01254-1},
  DOI = {10.1038/s41588-022-01254-1},
  number = {2},
  journal = {Nature Genetics},
  publisher = {Springer Science and Business Media LLC},
  author = {Fonseca, Marcos A. S. and Haro, Marcela and Wright, Kelly N. and Lin, Xianzhi and Abbasi, Forough and Sun, Jennifer and Hernandez, Lourdes and Orr, Natasha L. and Hong, Jooyoon and Choi-Kuaea, Yunhee and Maluf, Horacio M. and Balzer, Bonnie L. and Fishburn, Aaron and Hickey, Ryan and Cass, Ilana and Goodridge, Helen S. and Truong, Mireille and Wang, Yemin and Pisarska, Margareta D. and Dinh, Huy Q. and EL-Naggar, Amal and Huntsman, David G. and Anglesio, Michael S. and Goodman, Marc T. and Medeiros, Fabiola and Siedhoff, Matthew and Lawrenson, Kate},
  year = {2023},
  month = Jan,
  pages = {255–267}
}

Source: nature.com ↗ · License: CC BY-NC-SA 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB