Visium

Human peritoneum

GSM8279109

bronze Interactive viewer H&E desmoplastic small round cell tumor Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM8279109_GR4_PC_hires_image': DataArray[cyx] (3, 2000, 1935)
│     └── 'GSM8279109_GR4_PC_lowres_image': DataArray[cyx] (3, 600, 581)
├── Shapes
│     └── 'GSM8279109_GR4_PC': GeoDataFrame shape: (3583, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3583, 17943)
with coordinate systems:
    ▸ 'GSM8279109_GR4_PC', with elements:
        GSM8279109_GR4_PC_hires_image (Images), GSM8279109_GR4_PC_lowres_image (Images), GSM8279109_GR4_PC (Shapes)
    ▸ 'GSM8279109_GR4_PC_downscaled_hires', with elements:
        GSM8279109_GR4_PC_hires_image (Images), GSM8279109_GR4_PC (Shapes)
    ▸ 'GSM8279109_GR4_PC_downscaled_lowres', with elements:
        GSM8279109_GR4_PC_lowres_image (Images), GSM8279109_GR4_PC (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("TYTIhV1ULNYBd9RQ0000")
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("TYTIhV1ULNYBd9RQ")   # 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 TYTIhV1ULNYBd9RQ0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/TYTIhV1ULNYBd9RQ

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid TYTIhV1ULNYBd9RQ

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Single-cell multiomics profiling reveals heterogeneous transcriptional programs and microenvironment in Desmoplastic Small Round Cell Tumors [Visium: Spatial transcriptomics]→

6 datasets · other

Metadata

Technology: Visium
Organism: human
Tissue: peritoneum
Condition: desmoplastic small round cell tumor
Modality: RNA
Observations (spots): 3,583
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,935 × 2,000 px
mpp: 3.461 µm/px
Size: 35.4 MB
Sample ID: GSM8279109
License: NCBI GEO policy
Published: Apr 15, 2024

Cite this dataset

doi.org/10.1016/j.xcrm.2024.101582 ↗

BibTeX

@article{Henon_2024,
  title = {Single-cell multiomics profiling reveals heterogeneous transcriptional programs and microenvironment in DSRCTs},
  volume = {5},
  ISSN = {2666-3791},
  url = {http://dx.doi.org/10.1016/j.xcrm.2024.101582},
  DOI = {10.1016/j.xcrm.2024.101582},
  number = {6},
  journal = {Cell Reports Medicine},
  publisher = {Elsevier BV},
  author = {Henon, Clémence and Vibert, Julien and Eychenne, Thomas and Gruel, Nadège and Colmet-Daage, Léo and Ngo, Carine and Garrido, Marlène and Dorvault, Nicolas and Marques Da Costa, Maria Eugenia and Marty, Virginie and Signolle, Nicolas and Marchais, Antonin and Herbel, Noé and Kawai-Kawachi, Asuka and Lenormand, Madison and Astier, Clémence and Chabanon, Roman and Verret, Benjamin and Bahleda, Rastislav and Le Cesne, Axel and Mechta-Grigoriou, Fatima and Faron, Matthieu and Honoré, Charles and Delattre, Olivier and Waterfall, Joshua J. and Watson, Sarah and Postel-Vinay, Sophie},
  year = {2024},
  month = June,
  pages = {101582}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB