Visium

Mouse popliteal lymph node

GSM5841551

bronze Interactive viewer H&E rheumatoid arthritis Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM5841551_Early_hires_image': DataArray[cyx] (3, 2000, 2000)
│     └── 'GSM5841551_Early_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│     └── 'GSM5841551_Early': GeoDataFrame shape: (1489, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1489, 32287)
with coordinate systems:
    ▸ 'GSM5841551_Early', with elements:
        GSM5841551_Early_hires_image (Images), GSM5841551_Early_lowres_image (Images), GSM5841551_Early (Shapes)
    ▸ 'GSM5841551_Early_downscaled_hires', with elements:
        GSM5841551_Early_hires_image (Images), GSM5841551_Early (Shapes)
    ▸ 'GSM5841551_Early_downscaled_lowres', with elements:
        GSM5841551_Early_lowres_image (Images), GSM5841551_Early (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("5OzAaPexsBfEfVRQ0000")
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("5OzAaPexsBfEfVRQ")   # 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 5OzAaPexsBfEfVRQ0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/5OzAaPexsBfEfVRQ

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 5OzAaPexsBfEfVRQ

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial Transcriptomics of Joint-Draining Popliteal Lymph Nodes from Wild-type and TNF-Tg Mice with Early and Advanced Inflammatory-Erosive Arthritis→

4 datasets · time course

Metadata

Technology: Visium
Organism: mouse
Tissue: popliteal lymph node
Condition: rheumatoid arthritis
Modality: RNA
Observations (spots): 1,489
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 2,000 px
mpp: 3.266 µm/px
Size: 11.3 MB
Sample ID: GSM5841551
License: NCBI GEO policy
Published: Aug 14, 2023

Cite this dataset

doi.org/10.3389/fimmu.2023.1237498 ↗

BibTeX

@article{Kenney_2023,
  title = {Multi-omics analysis identifies IgG2b class-switching with ALCAM-CD6 co-stimulation in joint-draining lymph nodes during advanced inflammatory-erosive arthritis},
  volume = {14},
  ISSN = {1664-3224},
  url = {http://dx.doi.org/10.3389/fimmu.2023.1237498},
  DOI = {10.3389/fimmu.2023.1237498},
  journal = {Frontiers in Immunology},
  publisher = {Frontiers Media SA},
  author = {Kenney, H. Mark and Rangel-Moreno, Javier and Peng, Yue and Chen, Kiana L. and Bruno, Jennifer and Embong, Abdul and Pritchett, Elizabeth and Fox, Jeffrey I. and Becerril-Villanueva, Enrique and Gamboa-Domínguez, Armando and Quataert, Sally and Muthukrishnan, Gowrishankar and Wood, Ronald W. and Korman, Benjamin D. and Anolik, Jennifer H. and Xing, Lianping and Ritchlin, Christopher T. and Schwarz, Edward M. and Wu, Chia-Lung},
  year = {2023},
  month = Aug
}

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