Visium

Human lung

MEND87

silver Interactive viewer H&E ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     └── 'MEND87_full_image': DataTree[cyx] (3, 46592, 48640), (3, 23296, 24320), (3, 11648, 12160), (3, 5824, 6080)
├── Shapes
│     └── 'MEND87_spots': GeoDataFrame shape: (1508, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1508, 36601)
with coordinate systems:
    ▸ 'global', with elements:
        MEND87_full_image (Images), MEND87_spots (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("MiZXG1W8WzoKu5qT")   # 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/MiZXG1W8WzoKu5qT

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid MiZXG1W8WzoKu5qT

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["MiZXG1W8WzoKu5qT0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part of collection

Spatially resolved transcriptomic profiling of degraded→

35 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: lung
Modality: RNA
Observations: 1,508
Tier: silver
Chemistry: Visium v1
Magnification: 40x
Resolution: 48,640 × 46,592 px
mpp: 0.172 µm/px
Size: 3.2 GB
Sample ID: MEND87
License: CC BY-NC-SA 4.0 NonCommercial
Published: Dec 20, 2022

Cite this dataset

doi.org/10.1038/s41467-023-36071-5 ↗

BibTeX

@article{Mirzazadeh_2023,
  title = {Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples},
  volume = {14},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-023-36071-5},
  DOI = {10.1038/s41467-023-36071-5},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Mirzazadeh, Reza and Andrusivova, Zaneta and Larsson, Ludvig and Newton, Phillip T. and Galicia, Leire Alonso and Abalo, Xesús M. and Avijgan, Mahtab and Kvastad, Linda and Denadai-Souza, Alexandre and Stakenborg, Nathalie and Firsova, Alexandra B. and Shamikh, Alia and Jurek, Aleksandra and Schultz, Niklas and Nistér, Monica and Samakovlis, Christos and Boeckxstaens, Guy and Lundeberg, Joakim},
  year = {2023},
  month = Jan
}

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