spatial transcriptomics
Human breast
SPA1
bronze Interactive viewer H&E invasive ductal breast carcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'SPA1_full_image': DataTree[cyx] (3, 9931, 9277), (3, 4965, 4638), (3, 2482, 2319), (3, 1241, 1159)
├── Shapes
│ └── 'SPA1_spots': GeoDataFrame shape: (264, 2) (2D shapes)
└── Tables
└── 'table': AnnData (264, 14616)
with coordinate systems:
▸ 'global', with elements:
SPA1_full_image (Images), SPA1_spots (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("sJ28VA6yFx5gcGZg") # 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/sJ28VA6yFx5gcGZg
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid sJ28VA6yFx5gcGZg Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["sJ28VA6yFx5gcGZg0002"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Integrating spatial gene expression and breast tumour morphology via deep learning→ 84 datasets · comparison
Metadata
- Technology
- spatial transcriptomics
- Organism
- human
- Tissue
- breast
- Condition
- invasive ductal breast carcinoma
- Modality
- RNA
- Observations
- 264
- Tier
- bronze
- Chemistry
- ST 1k
- Resolution
- 9,277 × 9,931 px
- mpp
- 0.685 µm/px
- Size
- 104 MB
- Sample ID
- SPA1
- License
- CC BY-NC-SA 4.0 NonCommercial
- Published
- Jul 1, 2016
Cite this dataset
BibTeX
@article{Ståhl_2016,
title = {Visualization and analysis of gene expression in tissue sections by spatial transcriptomics},
author = {Ståhl, PL and Salmén, F and Vickovic, S and Lundmark, A and Navarro, JF and Magnusson, J and Giacomello, S and Asp, M and Westholm, JO and Huss, M and Mollbrink, A and Linnarsson, S and Codeluppi, S and Borg, Å and Pontén, F and Costea, PI and Sahlén, P and Mulder, J and Bergmann, O and Lundeberg, J and Frisén, J},
journal = {Science (New York, N.Y.)},
year = {2016},
volume = {353},
number = {6294},
pages = {78-82},
pmid = {27365449}
}Source: NCBI GEO ↗ · License: CC BY-NC-SA 4.0 ↗ · Reprocessed to SpatialData by SpatialData-DB