Visium

Mouse thymus

GSM7097913

silver Interactive viewer H&E asthma Provisional

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM7097913_Ctrl_bio1_tech1_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM7097913_Ctrl_bio1_tech1_hires_image': DataArray[cyx] (3, 2000, 1777)
│     └── 'GSM7097913_Ctrl_bio1_tech1_lowres_image': DataArray[cyx] (3, 600, 533)
├── Shapes
│     └── 'GSM7097913_Ctrl_bio1_tech1': GeoDataFrame shape: (2830, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2830, 19465)
with coordinate systems:
    ▸ 'GSM7097913_Ctrl_bio1_tech1', with elements:
        GSM7097913_Ctrl_bio1_tech1_full_image (Images), GSM7097913_Ctrl_bio1_tech1_hires_image (Images), GSM7097913_Ctrl_bio1_tech1_lowres_image (Images), GSM7097913_Ctrl_bio1_tech1 (Shapes)
    ▸ 'GSM7097913_Ctrl_bio1_tech1_downscaled_hires', with elements:
        GSM7097913_Ctrl_bio1_tech1_hires_image (Images), GSM7097913_Ctrl_bio1_tech1 (Shapes)
    ▸ 'GSM7097913_Ctrl_bio1_tech1_downscaled_lowres', with elements:
        GSM7097913_Ctrl_bio1_tech1_lowres_image (Images), GSM7097913_Ctrl_bio1_tech1 (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("HFmL9bDMbyGNgPxp")   # 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/HFmL9bDMbyGNgPxp

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid HFmL9bDMbyGNgPxp

Load with the Python client

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

ds = Catalog().cohort(uids=["HFmL9bDMbyGNgPxp0000"])[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

The transcription factor Zfp281 serves redundantly with Zfp148 to support CD4+ T cell development and functions [Thymus Visium]→

6 datasets · comparison

Metadata

Technology: Visium
Organism: mouse
Tissue: thymus
Condition: asthma
Modality: RNA
Observations: 2,830
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 1,777 × 2,000 px
mpp: 4.383 µm/px
Size: 87 MB
Sample ID: GSM7097913
License: NCBI GEO policy
Published: Nov 13, 2023

Cite this dataset

doi.org/10.1126/sciimmunol.adi9066 ↗

BibTeX

@article{Chopp_2023,
  title = {Zfp281 and Zfp148 control CD4
            <sup>+</sup>
            T cell thymic development and T
            <sub>H</sub>
            2 functions},
  volume = {8},
  ISSN = {2470-9468},
  url = {http://dx.doi.org/10.1126/sciimmunol.adi9066},
  DOI = {10.1126/sciimmunol.adi9066},
  number = {89},
  journal = {Science Immunology},
  publisher = {American Association for the Advancement of Science (AAAS)},
  author = {Chopp, Laura B. and Zhu, Xiaoliang and Gao, Yayi and Nie, Jia and Singh, Jatinder and Kumar, Parimal and Young, Kelly Z. and Patel, Shil and Li, Caiyi and Balmaceno-Criss, Mariah and Vacchio, Melanie S. and Wang, Michael M. and Livak, Ferenc and Merchant, Juanita L. and Wang, Lie and Kelly, Michael C. and Zhu, Jinfang and Bosselut, Rémy},
  year = {2023},
  month = Nov
}

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