Visium

Human cerebellum

GSM9202346

bronze Interactive viewer H&E medulloblastoma Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9202346_SHH-3B_hires_image': DataArray[cyx] (3, 1989, 2000)
│     └── 'GSM9202346_SHH-3B_lowres_image': DataArray[cyx] (3, 597, 600)
├── Shapes
│     └── 'GSM9202346_SHH-3B': GeoDataFrame shape: (3613, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (3613, 17943)
with coordinate systems:
    ▸ 'GSM9202346_SHH-3B', with elements:
        GSM9202346_SHH-3B_hires_image (Images), GSM9202346_SHH-3B_lowres_image (Images), GSM9202346_SHH-3B (Shapes)
    ▸ 'GSM9202346_SHH-3B_downscaled_hires', with elements:
        GSM9202346_SHH-3B_hires_image (Images), GSM9202346_SHH-3B (Shapes)
    ▸ 'GSM9202346_SHH-3B_downscaled_lowres', with elements:
        GSM9202346_SHH-3B_lowres_image (Images), GSM9202346_SHH-3B (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("QtU6p8h7QXwasrcG0000")
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("QtU6p8h7QXwasrcG")   # 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 QtU6p8h7QXwasrcG0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/QtU6p8h7QXwasrcG

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid QtU6p8h7QXwasrcG

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Unveiling Transcriptional Heterogeneity and Discovering Footprints in Pediatric Medulloblastoma via Spatial RNA-Seq and Machine Learning→

6 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: cerebellum
Condition: medulloblastoma
Modality: RNA
Observations (spots): 3,613
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 1,989 px
mpp: 3.331 µm/px
Size: 89.7 MB
Sample ID: GSM9202346
License: NCBI GEO policy
Published: Jul 22, 2026

Cite this dataset

doi.org/10.3390/ijms27135720 ↗

BibTeX

@article{Ju_rez_M_ndez_2026,
  title = {Identification of Markers on the Basis of Transcriptomic Analysis for Molecular Assignment of Medulloblastoma},
  volume = {27},
  ISSN = {1422-0067},
  url = {http://dx.doi.org/10.3390/ijms27135720},
  DOI = {10.3390/ijms27135720},
  number = {13},
  journal = {International Journal of Molecular Sciences},
  publisher = {MDPI AG},
  author = {Juárez-Méndez, Sergio and Vázquez-Jiménez, Aarón and Ramírez-Chiquito, Josselen Carina and Villegas-Ruíz, Vanessa and Niembro-Zuñiga, Ana Maria and Farfán-Morales, José Eduardo and Marhx-Bracho, Alfonso and Krötzsch, Edgar and Rodríguez-Morales, Miguel and Segura-Solís, Emma and Perezpeña-Diazconti, Mario and Ridaura-Sanz, Cecilia and Rivera-Luna, Roberto and Eguía-Aguilar, Pilar and Resendis-Antonio, Osbaldo and Melendez-Zajgla, Jorge},
  year = {2026},
  month = June,
  pages = {5720}
}

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