Visium

Human kidney

GSM7974484

bronze Interactive viewer H&E pauci-immune glomerulonephritis with ANCA Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM7974484_V4_A_hires_image': DataArray[cyx] (3, 2000, 1949)
│     └── 'GSM7974484_V4_A_lowres_image': DataArray[cyx] (3, 600, 585)
├── Shapes
│     └── 'GSM7974484_V4_A': GeoDataFrame shape: (999, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (999, 17943)
with coordinate systems:
    ▸ 'GSM7974484_V4_A', with elements:
        GSM7974484_V4_A_hires_image (Images), GSM7974484_V4_A_lowres_image (Images), GSM7974484_V4_A (Shapes)
    ▸ 'GSM7974484_V4_A_downscaled_hires', with elements:
        GSM7974484_V4_A_hires_image (Images), GSM7974484_V4_A (Shapes)
    ▸ 'GSM7974484_V4_A_downscaled_lowres', with elements:
        GSM7974484_V4_A_lowres_image (Images), GSM7974484_V4_A (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("HfNKlKqk3U4KgwIJ0000")
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("HfNKlKqk3U4KgwIJ")   # 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 HfNKlKqk3U4KgwIJ0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/HfNKlKqk3U4KgwIJ

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid HfNKlKqk3U4KgwIJ

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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Part of collection

Immune profiling-based targeting of pathogenic T cells with ustekinumab in ANCA-associated glomerulonephritis→

23 datasets · comparison

Metadata

Technology: Visium
Organism: human
Tissue: kidney
Condition: pauci-immune glomerulonephritis with ANCA
Modality: RNA
Observations (spots): 999
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 1,949 × 2,000 px
mpp: 3.574 µm/px
Size: 8.6 MB
Sample ID: GSM7974484
License: NCBI GEO policy
Published: Jun 28, 2024

Cite this dataset

doi.org/10.1038/s41467-024-52525-w ↗

BibTeX

@article{Engesser_2024,
  title = {Immune profiling-based targeting of pathogenic T cells with ustekinumab in ANCA-associated glomerulonephritis},
  volume = {15},
  ISSN = {2041-1723},
  url = {http://dx.doi.org/10.1038/s41467-024-52525-w},
  DOI = {10.1038/s41467-024-52525-w},
  number = {1},
  journal = {Nature Communications},
  publisher = {Springer Science and Business Media LLC},
  author = {Engesser, Jonas and Khatri, Robin and Schaub, Darius P. and Zhao, Yu and Paust, Hans-Joachim and Sultana, Zeba and Asada, Nariaki and Riedel, Jan-Hendrik and Sivayoganathan, Varshi and Peters, Anett and Kaffke, Anna and Jauch-Speer, Saskia-Larissa and Goldbeck-Strieder, Thiago and Puelles, Victor G. and Wenzel, Ulrich O. and Steinmetz, Oliver M. and Hoxha, Elion and Turner, Jan-Eric and Mittrücker, Hans-Willi and Wiech, Thorsten and Huber, Tobias B. and Bonn, Stefan and Krebs, Christian F. and Panzer, Ulf},
  year = {2024},
  month = Sept
}

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