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Vizgen MERSCOPE

Human unknown

GSM9238732

bronze Interactive viewer cancer Provisional
Human unknown preview
print(sdata)
SpatialData object
├── Points
│     └── 'merscope_region_0_transcripts': DataFrame with shape: (2877094, 9) (3D points)
├── Shapes
│     ├── 'merscope_region_0_polygons': GeoDataFrame shape: (33654, 9) (2D shapes)
│     └── 'merscope_region_0_polygons_for_vitessce': GeoDataFrame shape: (33654, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (33654, 255)
with coordinate systems:
    ▸ 'global', with elements:
        merscope_region_0_transcripts (Points), merscope_region_0_polygons (Shapes), merscope_region_0_polygons_for_vitessce (Shapes)
Python · sddb client
from sddb import Catalog

ds = Catalog().dataset("iIPlBukew3ay0wAA0000")
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("iIPlBukew3ay0wAA")   # 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 iIPlBukew3ay0wAA0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/iIPlBukew3ay0wAA

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid iIPlBukew3ay0wAA

License: NCBI GEO policy

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

Metadata

Technology
Vizgen MERSCOPE
Organism
human
Tissue
unknown
Condition
cancer
Modality
RNA
Observations (cells)
33,654
Transcripts
2,877,094
Tier
bronze
Size
81.1 MB
Sample ID
GSM9238732
License
NCBI GEO policy
Published
Oct 3, 2025

Cite this dataset

doi.org/10.1038/s41467-025-64990-y ↗
BibTeX
@article{geo:GSM9238732,
  doi = {10.1038/s41467-025-64990-y},
  title = {GSM9238732:region_1},
  year = {2025}
}

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