PhenoCycler-Fusion
Human bone marrow
25123538:AML3_1443_Scan1
silver Interactive viewer acute myeloid leukemia ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ └── 'AML3_1443_image': DataTree[cyx] (54, 15840, 8640), (54, 7920, 4320), (54, 3960, 2160), (54, 1980, 1080), (54, 990, 540)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (48381, 1) (2D shapes)
│ └── 'cell_boundaries_for_vitessce': GeoDataFrame shape: (48381, 2) (2D shapes)
└── Tables
└── 'table': AnnData (48381, 54)
with coordinate systems:
▸ 'global', with elements:
AML3_1443_image (Images), cell_boundaries (Shapes), cell_boundaries_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("HKRTO9iFERwm6Wxi0000")
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("HKRTO9iFERwm6Wxi") # 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 HKRTO9iFERwm6Wxi0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/HKRTO9iFERwm6Wxi
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid HKRTO9iFERwm6WxiLicense: CC0 1.0
Metadata
- Technology
- PhenoCycler-Fusion
- Organism
- human
- Tissue
- bone marrow
- Condition
- acute myeloid leukemia
- Modality
- protein
- Observations (cells)
- 48,381
- Tier
- silver
- Chemistry
- PhenoCycler-Fusion 1
- Staining
- IF
- Size
- 2.0 GB
- Sample ID
- 25123538:AML3_1443_Scan1
- License
- CC0 1.0
- Published
- Apr 12, 2024
Cite this dataset
doi.org/10.1016/j.cell.2024.04.013 ↗ BibTeX
@article{Bandyopadhyay_2024,
title = {Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging},
volume = {187},
ISSN = {0092-8674},
url = {http://dx.doi.org/10.1016/j.cell.2024.04.013},
DOI = {10.1016/j.cell.2024.04.013},
number = {12},
journal = {Cell},
publisher = {Elsevier BV},
author = {Bandyopadhyay, Shovik and Duffy, Michael P. and Ahn, Kyung Jin and Sussman, Jonathan H. and Pang, Minxing and Smith, David and Duncan, Gwendolyn and Zhang, Iris and Huang, Jeffrey and Lin, Yulieh and Xiong, Barbara and Imtiaz, Tamjid and Chen, Chia-Hui and Thadi, Anusha and Chen, Changya and Xu, Jason and Reichart, Melissa and Martinez, Zachary and Diorio, Caroline and Chen, Chider and Pillai, Vinodh and Snaith, Oraine and Oldridge, Derek and Bhattacharyya, Siddharth and Maillard, Ivan and Carroll, Martin and Nelson, Charles and Qin, Ling and Tan, Kai},
year = {2024},
month = June,
pages = {3120–3140.e29}
}Source: doi.org ↗ · License: CC0 1.0 ↗ · Reprocessed to SpatialData by SpatialData-DB