Visium
Human thymus
GSM6281321
bronze Interactive viewer H&E Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM6281321_S1_B1_raw_feature_bc_matrix_hires_image': DataArray[cyx] (3, 1872, 2000)
│ └── 'GSM6281321_S1_B1_raw_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 562, 600)
├── Shapes
│ └── 'GSM6281321_S1_B1_raw_feature_bc_matrix': GeoDataFrame shape: (4992, 2) (2D shapes)
└── Tables
└── 'table': AnnData (4992, 36601)
with coordinate systems:
▸ 'GSM6281321_S1_B1_raw_feature_bc_matrix', with elements:
GSM6281321_S1_B1_raw_feature_bc_matrix_hires_image (Images), GSM6281321_S1_B1_raw_feature_bc_matrix_lowres_image (Images), GSM6281321_S1_B1_raw_feature_bc_matrix (Shapes)
▸ 'GSM6281321_S1_B1_raw_feature_bc_matrix_downscaled_hires', with elements:
GSM6281321_S1_B1_raw_feature_bc_matrix_hires_image (Images), GSM6281321_S1_B1_raw_feature_bc_matrix (Shapes)
▸ 'GSM6281321_S1_B1_raw_feature_bc_matrix_downscaled_lowres', with elements:
GSM6281321_S1_B1_raw_feature_bc_matrix_lowres_image (Images), GSM6281321_S1_B1_raw_feature_bc_matrix (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("lMwXlGG9b6TceLB10000")
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("lMwXlGG9b6TceLB1") # 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 lMwXlGG9b6TceLB10000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/lMwXlGG9b6TceLB1
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid lMwXlGG9b6TceLB1License: NCBI GEO policy
Part of collection
Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection [Spatial transcriptomics]→ 8 datasets · other
Metadata
- Technology
- Visium
- Organism
- human
- Tissue
- thymus
- Modality
- RNA
- Observations (spots)
- 4,992
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,872 px
- mpp
- 3.466 µm/px
- Size
- 54.4 MB
- Sample ID
- GSM6281321
- License
- NCBI GEO policy
- Published
- Feb 1, 2023
Cite this dataset
doi.org/10.3389/fimmu.2022.1092028 ↗ BibTeX
@article{Heimli_2023,
title = {Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection},
volume = {13},
ISSN = {1664-3224},
url = {http://dx.doi.org/10.3389/fimmu.2022.1092028},
DOI = {10.3389/fimmu.2022.1092028},
journal = {Frontiers in Immunology},
publisher = {Frontiers Media SA},
author = {Heimli, Marte and Flåm, Siri Tennebø and Hjorthaug, Hanne Sagsveen and Trinh, Don and Frisk, Michael and Dumont, Karl-Andreas and Ribarska, Teodora and Tekpli, Xavier and Saare, Mario and Lie, Benedicte Alexandra},
year = {2023},
month = Jan
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB