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spatial ATAC-RNA-seq

Human hippocampal formation

GSM6206884

bronze Interactive viewer H&E ✓ Verified publication
spatial ATAC-RNA-seq
print(sdata) 
SpatialData object
├── Images
│     └── 'tissue_hires': DataArray[cyx] (3, 3952, 3953)
├── Shapes
│     ├── 'epi_spots': GeoDataFrame shape: (2500, 1) (2D shapes)
│     └── 'epi_spots_for_vitessce': GeoDataFrame shape: (2500, 2) (2D shapes)
└── Tables
      ├── 'epigenome_atac': AnnData (2500, 617665)
      └── 'table': AnnData (2500, 18734)
with coordinate systems:
    ▸ 'global', with elements:
        tissue_hires (Images), epi_spots (Shapes), epi_spots_for_vitessce (Shapes)
Install (Python 3.12+) Docs →
Python · sddb client 
from sddb import Catalog

ds = Catalog().dataset("7oVcKKU1o5xlGMKy0000")
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("7oVcKKU1o5xlGMKy")   # 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 7oVcKKU1o5xlGMKy0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/7oVcKKU1o5xlGMKy

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 7oVcKKU1o5xlGMKy
Install (Python 3.12+) Docs →

License: NCBI GEO policy

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1 datasets · publication

Metadata

Technology
spatial ATAC-RNA-seq
Organism
human
Tissue
hippocampal formation
Modality
RNA · Epigenomics
Observations (pixels)
2,500
Tier
bronze
Resolution
3,953 × 3,952 px
mpp
50.000 µm/px
Size
234 MB
Sample ID
GSM6206884
License
NCBI GEO policy
Published
Jan 29, 2023

Cite this dataset

doi.org/10.1038/s41586-023-05795-1 ↗
BibTeX 
@article{Zhang_2023,
  title = {Spatial epigenome–transcriptome co-profiling of mammalian tissues},
  volume = {616},
  ISSN = {1476-4687},
  url = {http://dx.doi.org/10.1038/s41586-023-05795-1},
  DOI = {10.1038/s41586-023-05795-1},
  number = {7955},
  journal = {Nature},
  publisher = {Springer Science and Business Media LLC},
  author = {Zhang, Di and Deng, Yanxiang and Kukanja, Petra and Agirre, Eneritz and Bartosovic, Marek and Dong, Mingze and Ma, Cong and Ma, Sai and Su, Graham and Bao, Shuozhen and Liu, Yang and Xiao, Yang and Rosoklija, Gorazd B. and Dwork, Andrew J. and Mann, J. John and Leong, Kam W. and Boldrini, Maura and Wang, Liya and Haeussler, Maximilian and Raphael, Benjamin J. and Kluger, Yuval and Castelo-Branco, Gonçalo and Fan, Rong},
  year = {2023},
  month = Mar,
  pages = {113–122}
}

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