Visium HD

Human kidney

GSM9802211

silver Interactive viewer H&E kidney disorder No linked publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9802211_square_016um_cytassist_image': DataArray[cyx] (3, 3002, 3199)
│     ├── 'GSM9802211_square_016um_hires_image': DataArray[cyx] (3, 4618, 6000)
│     └── 'GSM9802211_square_016um_lowres_image': DataArray[cyx] (3, 462, 600)
├── Shapes
│     ├── 'GSM9802211_square_016um_square_002um': GeoDataFrame shape: (3227445, 1) (2D shapes)
│     ├── 'GSM9802211_square_016um_square_008um': GeoDataFrame shape: (205569, 1) (2D shapes)
│     ├── 'GSM9802211_square_016um_square_016um': GeoDataFrame shape: (52724, 1) (2D shapes)
│     └── 'GSM9802211_square_016um_square_016um_for_vitessce': GeoDataFrame shape: (52724, 2) (2D shapes)
└── Tables
      ├── 'square_002um': AnnData (3227445, 18132)
      ├── 'square_008um': AnnData (205569, 18132)
      └── 'square_016um': AnnData (52724, 18132)
with coordinate systems:
    ▸ 'GSM9802211_square_016um', with elements:
        GSM9802211_square_016um_cytassist_image (Images), GSM9802211_square_016um_hires_image (Images), GSM9802211_square_016um_lowres_image (Images), GSM9802211_square_016um_square_002um (Shapes), GSM9802211_square_016um_square_008um (Shapes), GSM9802211_square_016um_square_016um (Shapes), GSM9802211_square_016um_square_016um_for_vitessce (Shapes)
    ▸ 'GSM9802211_square_016um_downscaled_hires', with elements:
        GSM9802211_square_016um_hires_image (Images), GSM9802211_square_016um_square_002um (Shapes), GSM9802211_square_016um_square_008um (Shapes), GSM9802211_square_016um_square_016um (Shapes), GSM9802211_square_016um_square_016um_for_vitessce (Shapes)
    ▸ 'GSM9802211_square_016um_downscaled_lowres', with elements:
        GSM9802211_square_016um_lowres_image (Images), GSM9802211_square_016um_square_002um (Shapes), GSM9802211_square_016um_square_008um (Shapes), GSM9802211_square_016um_square_016um (Shapes), GSM9802211_square_016um_square_016um_for_vitessce (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("CgXLPMQ7N6ziDcYg")   # 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/CgXLPMQ7N6ziDcYg

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid CgXLPMQ7N6ziDcYg

Load with the Python client

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

ds = Catalog().cohort(uids=["CgXLPMQ7N6ziDcYg0000"])[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

geo-gse334924-camr-kidney→

1 datasets · other

Metadata

Technology: Visium HD
Organism: human
Tissue: kidney
Condition: kidney disorder
Modality: RNA
Observations: 205,569
Tier: silver
Resolution: 6,000 × 4,618 px
mpp: 2.091 µm/px
Size: 518 MB
Sample ID: GSM9802211
License: unknown
Published: Jul 21, 2026

Cite this dataset

No linked publication found — please cite the data source: NCBI GEO ↗.

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